Martha Palmer

Also published as: Martha S. Palmer, Martha Stone Palmer


2021

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Predicate Representations and Polysemy in VerbNet Semantic Parsing
James Gung | Martha Palmer
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

Despite recent advances in semantic role labeling propelled by pre-trained text encoders like BERT, performance lags behind when applied to predicates observed infrequently during training or to sentences in new domains. In this work, we investigate how role labeling performance on low-frequency predicates and out-of-domain data can be further improved by using VerbNet, a verb lexicon that groups verbs into hierarchical classes based on shared syntactic and semantic behavior and defines semantic representations describing relations between arguments. We find that VerbNet classes provide an effective level of abstraction, improving generalization on low-frequency predicates by allowing them to learn from the training examples of other predicates belonging to the same class. We also find that joint training of VerbNet role labeling and predicate disambiguation of VerbNet classes for polysemous verbs leads to improvements in both tasks, naturally supporting the extraction of VerbNet’s semantic representations.

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Tuning Deep Active Learning for Semantic Role Labeling
Skatje Myers | Martha Palmer
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

Active learning has been shown to reduce annotation requirements for numerous natural language processing tasks, including semantic role labeling (SRL). SRL involves labeling argument spans for potentially multiple predicates in a sentence, which makes it challenging to aggregate the numerous decisions into a single score for determining new instances to annotate. In this paper, we apply two ways of aggregating scores across multiple predicates in order to choose query sentences with two methods of estimating model certainty: using the neural network’s outputs and using dropout-based Bayesian Active Learning by Disagreement. We compare these methods with three passive baselines — random sentence selection, random whole-document selection, and selecting sentences with the most predicates — and analyse the effect these strategies have on the learning curve with respect to reducing the number of annotated sentences and predicates to achieve high performance.

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SemLink 2.0: Chasing Lexical Resources
Kevin Stowe | Jenette Preciado | Kathryn Conger | Susan Windisch Brown | Ghazaleh Kazeminejad | James Gung | Martha Palmer
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

The SemLink resource provides mappings between a variety of lexical semantic ontologies, each with their strengths and weaknesses. To take advantage of these differences, the ability to move between resources is essential. This work describes advances made to improve the usability of the SemLink resource: the automatic addition of new instances and mappings, manual corrections, sense-based vectors and collocation information, and architecture built to automatically update the resource when versions of the underlying resources change. These updates improve coverage, provide new tools to leverage the capabilities of these resources, and facilitate seamless updates, ensuring the consistency and applicability of these mappings in the future.

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What Would a Teacher Do? Predicting Future Talk Moves
Ananya Ganesh | Martha Palmer | Katharina Kann
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang | Manling Li | Xuan Wang | Nikolaus Parulian | Guangxing Han | Jiawei Ma | Jingxuan Tu | Ying Lin | Ranran Haoran Zhang | Weili Liu | Aabhas Chauhan | Yingjun Guan | Bangzheng Li | Ruisong Li | Xiangchen Song | Yi Fung | Heng Ji | Jiawei Han | Shih-Fu Chang | James Pustejovsky | Jasmine Rah | David Liem | Ahmed ELsayed | Martha Palmer | Clare Voss | Cynthia Schneider | Boyan Onyshkevych
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports.

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RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
Haoyang Wen | Ying Lin | Tuan Lai | Xiaoman Pan | Sha Li | Xudong Lin | Ben Zhou | Manling Li | Haoyu Wang | Hongming Zhang | Xiaodong Yu | Alexander Dong | Zhenhailong Wang | Yi Fung | Piyush Mishra | Qing Lyu | Dídac Surís | Brian Chen | Susan Windisch Brown | Martha Palmer | Chris Callison-Burch | Carl Vondrick | Jiawei Han | Dan Roth | Shih-Fu Chang | Heng Ji
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video). The system advances state-of-the-art from two aspects: (1) extending from sentence-level event extraction to cross-document cross-lingual cross-media event extraction, coreference resolution and temporal event tracking; (2) using human curated event schema library to match and enhance the extraction output. We have made the dockerlized system publicly available for research purpose at GitHub, with a demo video.

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AutoAspect: Automatic Annotation of Tense and Aspect for Uniform Meaning Representations
Daniel Chen | Martha Palmer | Meagan Vigus
Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

We present AutoAspect, a novel, rule-based annotation tool for labeling tense and aspect. The pilot version annotates English data. The aspect labels are designed specifically for Uniform Meaning Representations (UMR), an annotation schema that aims to encode crosslingual semantic information. The annotation tool combines syntactic and semantic cues to assign aspects on a sentence-by-sentence basis, following a sequence of rules that each output a UMR aspect. Identified events proceed through the sequence until they are assigned an aspect. We achieve a recall of 76.17% for identifying UMR events and an accuracy of 62.57% on all identified events, with high precision values for 2 of the aspect labels.

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Automatic Entity State Annotation using the VerbNet Semantic Parser
Ghazaleh Kazeminejad | Martha Palmer | Tao Li | Vivek Srikumar
Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

Tracking entity states is a natural language processing task assumed to require human annotation. In order to reduce the time and expenses associated with annotation, we introduce a new method to automatically extract entity states, including location and existence state of entities, following Dalvi et al. (2018) and Tandon et al. (2020). For this purpose, we rely primarily on the semantic representations generated by the state of the art VerbNet parser (Gung, 2020), and extract the entities (event participants) and their states, based on the semantic predicates of the generated VerbNet semantic representation, which is in propositional logic format. For evaluation, we used ProPara (Dalvi et al., 2018), a reading comprehension dataset which is annotated with entity states in each sentence, and tracks those states in paragraphs of natural human-authored procedural texts. Given the presented limitations of the method, the peculiarities of the ProPara dataset annotations, and that our system, Lexis, makes no use of task-specific training data and relies solely on VerbNet, the results are promising, showcasing the value of lexical resources.

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TopGuNN: Fast NLP Training Data Augmentation using Large Corpora
Rebecca Iglesias-Flores | Megha Mishra | Ajay Patel | Akanksha Malhotra | Reno Kriz | Martha Palmer | Chris Callison-Burch
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

Acquiring training data for natural language processing systems can be expensive and time-consuming. Given a few training examples crafted by experts, large corpora can be mined for thousands of semantically similar examples that provide useful variability to improve model generalization. We present TopGuNN, a fast contextualized k-NN retrieval system that can efficiently index and search over contextual embeddings generated from large corpora. TopGuNN is demonstrated for a training data augmentation use case over the Gigaword corpus. Using approximate k-NN and an efficient architecture, TopGuNN performs queries over an embedding space of 4.63TB (approximately 1.5B embeddings) in less than a day.

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Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation
Zixuan Zhang | Nikolaus Parulian | Heng Ji | Ahmed Elsayed | Skatje Myers | Martha Palmer
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Biomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and events urgently requires domain-specific background knowledge. In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers. We perform Abstract Meaning Representation (AMR) to compress the wide context to uncover a clear semantic structure for each complex sentence. Besides, we construct the sentence-level knowledge graph from an external knowledge base and use it to enrich the AMR graph to improve the model’s understanding of complex scientific concepts. We use an edge-conditioned graph attention network to encode the knowledge-enriched AMR graph for biomedical IE tasks. Experiments on the GENIA 2011 dataset show that the AMR and external knowledge have contributed 1.8% and 3.0% absolute F-score gains respectively. In order to evaluate the impact of our approach on real-world problems that involve topic-specific fine-grained knowledge elements, we have also created a new ontology and annotated corpus for entity and event extraction for the COVID-19 scientific literature, which can serve as a new benchmark for the biomedical IE community.

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A Graphical Interface for Curating Schemas
Piyush Mishra | Akanksha Malhotra | Susan Windisch Brown | Martha Palmer | Ghazaleh Kazeminejad
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

Much past work has focused on extracting information like events, entities, and relations from documents. Very little work has focused on analyzing these results for better model understanding. In this paper, we introduce a curation interface that takes an Information Extraction (IE) system’s output in a pre-defined format and generates a graphical representation of its elements. The interface supports editing while curating schemas for complex events like Improvised Explosive Device (IED) based scenarios. We identify various schemas that either have linear event chains or contain parallel events with complicated temporal ordering. We iteratively update an induced schema to uniquely identify events specific to it, add optional events around them, and prune unnecessary events. The resulting schemas are improved and enriched versions of the machine-induced versions.

2020

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Structured Tuning for Semantic Role Labeling
Tao Li | Parth Anand Jawale | Martha Palmer | Vivek Srikumar
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores. These improvements are due to expressive input representations, which, at least at the surface, are orthogonal to knowledge-rich constrained decoding mechanisms that helped linear SRL models. Introducing the benefits of structure to inform neural models presents a methodological challenge. In this paper, we present a structured tuning framework to improve models using softened constraints only at training time. Our framework leverages the expressiveness of neural networks and provides supervision with structured loss components. We start with a strong baseline (RoBERTa) to validate the impact of our approach, and show that our framework outperforms the baseline by learning to comply with declarative constraints. Additionally, our experiments with smaller training sizes show that we can achieve consistent improvements under low-resource scenarios.

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Defining and Learning Refined Temporal Relations in the Clinical Narrative
Kristin Wright-Bettner | Chen Lin | Timothy Miller | Steven Bethard | Dmitriy Dligach | Martha Palmer | James H. Martin | Guergana Savova
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus- and domain- agnostic.

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Spatial AMR: Expanded Spatial Annotation in the Context of a Grounded Minecraft Corpus
Julia Bonn | Martha Palmer | Zheng Cai | Kristin Wright-Bettner
Proceedings of the 12th Language Resources and Evaluation Conference

This paper presents an expansion to the Abstract Meaning Representation (AMR) annotation schema that captures fine-grained semantically and pragmatically derived spatial information in grounded corpora. We describe a new lexical category conceptualization and set of spatial annotation tools built in the context of a multimodal corpus consisting of 170 3D structure-building dialogues between a human architect and human builder in Minecraft. Minecraft provides a particularly beneficial spatial relation-elicitation environment because it automatically tracks locations and orientations of objects and avatars in the space according to an absolute Cartesian coordinate system. Through a two-step process of sentence-level and document-level annotation designed to capture implicit information, we leverage these coordinates and bearings in the AMRs in combination with spatial framework annotation to ground the spatial language in the dialogues to absolute space.

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From Spatial Relations to Spatial Configurations
Soham Dan | Parisa Kordjamshidi | Julia Bonn | Archna Bhatia | Zheng Cai | Martha Palmer | Dan Roth
Proceedings of the 12th Language Resources and Evaluation Conference

Spatial Reasoning from language is essential for natural language understanding. Supporting it requires a representation scheme that can capture spatial phenomena encountered in language as well as in images and videos.Existing spatial representations are not sufficient for describing spatial configurations used in complex tasks. This paper extends the capabilities of existing spatial representation languages and increases coverage of the semantic aspects that are needed to ground spatial meaning of natural language text in the world. Our spatial relation language is able to represent a large, comprehensive set of spatial concepts crucial for reasoning and is designed to support composition of static and dynamic spatial configurations. We integrate this language with the Abstract Meaning Representation (AMR) annotation schema and present a corpus annotated by this extended AMR. To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.

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The Russian PropBank
Sarah Moeller | Irina Wagner | Martha Palmer | Kathryn Conger | Skatje Myers
Proceedings of the 12th Language Resources and Evaluation Conference

This paper presents a proposition bank for Russian (RuPB), a resource for semantic role labeling (SRL). The motivating goal for this resource is to automatically project semantic role labels from English to Russian. This paper describes frame creation strategies, coverage, and the process of sense disambiguation. It discusses language-specific issues that complicated the process of building the PropBank and how these challenges were exploited as language-internal guidance for consistency and coherence.

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Proceedings of the Second International Workshop on Designing Meaning Representations
Nianwen Xue | Johan Bos | William Croft | Jan Hajič | Chu-Ren Huang | Stephan Oepen | Martha Palmer | James Pustejovsky
Proceedings of the Second International Workshop on Designing Meaning Representations

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Leveraging Non-Specialists for Accurate and Time Efficient AMR Annotation
Mary Martin | Cecilia Mauceri | Martha Palmer | Christoffer Heckman
Proceedings of the LREC 2020 Workshop on "Citizen Linguistics in Language Resource Development"

Abstract Meaning Representations (AMRs), a syntax-free representation of phrase semantics are useful for capturing the meaning of a phrase and reflecting the relationship between concepts that are referred to. However, annotating AMRs are time consuming and expensive. The existing annotation process requires expertly trained workers who have knowledge of an extensive set of guidelines for parsing phrases. In this paper, we propose a cost-saving two-step process for the creation of a corpus of AMR-phrase pairs for spatial referring expressions. The first step uses non-specialists to perform simple annotations that can be leveraged in the second step to accelerate the annotation performed by the experts. We hypothesize that our process will decrease the cost per annotation and improve consistency across annotators. Few corpora of spatial referring expressions exist and the resulting language resource will be valuable for referring expression comprehension and generation modeling.

2019

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Syntactic composition and selectional preferences in Hindi Light Verb Constructions
Ashwini Vaidya | Martha Palmer
Linguistic Issues in Language Technology, Volume 17, 2019

Previous work on light verb constructions (e.g. chorii kar ‘theft do; steal’) in Hindi describes their syntactic formation via co-predication (Ahmed et al., 2012, Butt, 2014). This implies that both noun and light verb contribute their arguments, and these overlapping argument structures must be composed in the syntax. In this paper, we present a co-predication analysis using Tree-Adjoining Grammar, which models syntactic composition and semantic selectional preferences without transformations (deletion or argument identification). The analysis has two key components (i) an underspecified category for the nominal and (ii) combinatorial constraints on the noun and light verb to specify selectional preferences. The former has the advantage of syntactic composition without argument identification and the latter prevents over-generalization, while recognizing the semantic contribution of both predicates. This work additionally accounts for the agreement facts for the Hindi LVC.

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Cross-document coreference: An approach to capturing coreference without context
Kristin Wright-Bettner | Martha Palmer | Guergana Savova | Piet de Groen | Timothy Miller
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

This paper discusses a cross-document coreference annotation schema that was developed to further automatic extraction of timelines in the clinical domain. Lexical senses and coreference choices are determined largely by context, but cross-document work requires reasoning across contexts that are not necessarily coherent. We found that an annotation approach that relies less on context-guided annotator intuitions and more on schematic rules was most effective in creating meaningful and consistent cross-document relations.

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Proceedings of the First International Workshop on Designing Meaning Representations
Nianwen Xue | William Croft | Jan Hajic | Chu-Ren Huang | Stephan Oepen | Martha Palmer | James Pustejovksy
Proceedings of the First International Workshop on Designing Meaning Representations

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ClearTAC: Verb Tense, Aspect, and Form Classification Using Neural Nets
Skatje Myers | Martha Palmer
Proceedings of the First International Workshop on Designing Meaning Representations

This paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs. The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information. This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the Propbank I dataset.

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VerbNet Representations: Subevent Semantics for Transfer Verbs
Susan Windisch Brown | Julia Bonn | James Gung | Annie Zaenen | James Pustejovsky | Martha Palmer
Proceedings of the First International Workshop on Designing Meaning Representations

This paper announces the release of a new version of the English lexical resource VerbNet with substantially revised semantic representations designed to facilitate computer planning and reasoning based on human language. We use the transfer of possession and transfer of information event representations to illustrate both the general framework of the representations and the types of nuances the new representations can capture. These representations use a Generative Lexicon-inspired subevent structure to track attributes of event participants across time, highlighting oppositions and temporal and causal relations among the subevents.

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Explaining Simple Natural Language Inference
Aikaterini-Lida Kalouli | Annebeth Buis | Livy Real | Martha Palmer | Valeria de Paiva
Proceedings of the 13th Linguistic Annotation Workshop

The vast amount of research introducing new corpora and techniques for semi-automatically annotating corpora shows the important role that datasets play in today’s research, especially in the machine learning community. This rapid development raises concerns about the quality of the datasets created and consequently of the models trained, as recently discussed with respect to the Natural Language Inference (NLI) task. In this work we conduct an annotation experiment based on a small subset of the SICK corpus. The experiment reveals several problems in the annotation guidelines, and various challenges of the NLI task itself. Our quantitative evaluation of the experiment allows us to assign our empirical observations to specific linguistic phenomena and leads us to recommendations for future annotation tasks, for NLI and possibly for other tasks.

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Enhancing biomedical word embeddings by retrofitting to verb clusters
Billy Chiu | Simon Baker | Martha Palmer | Anna Korhonen
Proceedings of the 18th BioNLP Workshop and Shared Task

Verbs play a fundamental role in many biomed-ical tasks and applications such as relation and event extraction. We hypothesize that performance on many downstream tasks can be improved by aligning the input pretrained embeddings according to semantic verb classes.In this work, we show that by using semantic clusters for verbs, a large lexicon of verbclasses derived from biomedical literature, weare able to improve the performance of common pretrained embeddings in downstream tasks by retrofitting them to verb classes. We present a simple and computationally efficient approach using a widely-available “off-the-shelf” retrofitting algorithm to align pretrained embeddings according to semantic verb clusters. We achieve state-of-the-art results on text classification and relation extraction tasks.

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Linguistic Analysis Improves Neural Metaphor Detection
Kevin Stowe | Sarah Moeller | Laura Michaelis | Martha Palmer
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

In the field of metaphor detection, deep learning systems are the ubiquitous and achieve strong performance on many tasks. However, due to the complicated procedures for manually identifying metaphors, the datasets available are relatively small and fraught with complications. We show that using syntactic features and lexical resources can automatically provide additional high-quality training data for metaphoric language, and this data can cover gaps and inconsistencies in metaphor annotation, improving state-of-the-art word-level metaphor identification. This novel application of automatically improving training data improves classification across numerous tasks, and reconfirms the necessity of high-quality data for deep learning frameworks.

2018

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Automatically Extracting Qualia Relations for the Rich Event Ontology
Ghazaleh Kazeminejad | Claire Bonial | Susan Windisch Brown | Martha Palmer
Proceedings of the 27th International Conference on Computational Linguistics

Commonsense, real-world knowledge about the events that entities or “things in the world” are typically involved in, as well as part-whole relationships, is valuable for allowing computational systems to draw everyday inferences about the world. Here, we focus on automatically extracting information about (1) the events that typically bring about certain entities (origins), (2) the events that are the typical functions of entities, and (3) part-whole relationships in entities. These correspond to the agentive, telic and constitutive qualia central to the Generative Lexicon. We describe our motivations and methods for extracting these qualia relations from the Suggested Upper Merged Ontology (SUMO) and show that human annotators overwhelmingly find the information extracted to be reasonable. Because ontologies provide a way of structuring this information and making it accessible to agents and computational systems generally, efforts are underway to incorporate the extracted information to an ontology hub of Natural Language Processing semantic role labeling resources, the Rich Event Ontology.

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AMR Beyond the Sentence: the Multi-sentence AMR corpus
Tim O’Gorman | Michael Regan | Kira Griffitt | Ulf Hermjakob | Kevin Knight | Martha Palmer
Proceedings of the 27th International Conference on Computational Linguistics

There are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference and similar phenomena (bridging and implicit roles) on top of gold Abstract Meaning Representations of sentence-level semantics. We present a new corpus of this annotation, with analysis of its quality, alongside a plausible baseline for comparison. It is hoped that this Multi-Sentence AMR corpus (MS-AMR) may become a feasible method for developing rich representations of document meaning, useful for tasks such as information extraction and question answering.

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Leveraging Syntactic Constructions for Metaphor Identification
Kevin Stowe | Martha Palmer
Proceedings of the Workshop on Figurative Language Processing

Identification of metaphoric language in text is critical for generating effective semantic representations for natural language understanding. Computational approaches to metaphor identification have largely relied on heuristic based models or feature-based machine learning, using hand-crafted lexical resources coupled with basic syntactic information. However, recent work has shown the predictive power of syntactic constructions in determining metaphoric source and target domains (Sullivan 2013). Our work intends to explore syntactic constructions and their relation to metaphoric language. We undertake a corpus-based analysis of predicate-argument constructions and their metaphoric properties, and attempt to effectively represent syntactic constructions as features for metaphor processing, both in identifying source and target domains and in distinguishing metaphoric words from non-metaphoric.

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Improving Classification of Twitter Behavior During Hurricane Events
Kevin Stowe | Jennings Anderson | Martha Palmer | Leysia Palen | Ken Anderson
Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media

A large amount of social media data is generated during natural disasters, and identifying the relevant portions of this data is critical for researchers attempting to understand human behavior, the effects of information sources, and preparatory actions undertaken during these events. In order to classify human behavior during hazard events, we employ machine learning for two tasks: identifying hurricane related tweets and classifying user evacuation behavior during hurricanes. We show that feature-based and deep learning methods provide different benefits for tweet classification, and ensemble-based methods using linguistic, temporal, and geospatial features can effectively classify user behavior.

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Proceedings of the Workshop Events and Stories in the News 2018
Tommaso Caselli | Ben Miller | Marieke van Erp | Piek Vossen | Martha Palmer | Eduard Hovy | Teruko Mitamura | David Caswell | Susan W. Brown | Claire Bonial
Proceedings of the Workshop Events and Stories in the News 2018

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Developing and Evaluating Annotation Procedures for Twitter Data during Hazard Events
Kevin Stowe | Martha Palmer | Jennings Anderson | Marina Kogan | Leysia Palen | Kenneth M. Anderson | Rebecca Morss | Julie Demuth | Heather Lazrus
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

When a hazard such as a hurricane threatens, people are forced to make a wide variety of decisions, and the information they receive and produce can influence their own and others’ actions. As social media grows more popular, an increasing number of people are using social media platforms to obtain and share information about approaching threats and discuss their interpretations of the threat and their protective decisions. This work aims to improve understanding of natural disasters through social media and provide an annotation scheme to identify themes in user’s social media behavior and facilitate efforts in supervised machine learning. To that end, this work has three contributions: (1) the creation of an annotation scheme to consistently identify hazard-related themes in Twitter, (2) an overview of agreement rates and difficulties in identifying annotation categories, and (3) a public release of both the dataset and guidelines developed from this scheme.

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Integrating Generative Lexicon Event Structures into VerbNet
Susan Windisch Brown | James Pustejovsky | Annie Zaenen | Martha Palmer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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The New Propbank: Aligning Propbank with AMR through POS Unification
Tim O’Gorman | Sameer Pradhan | Martha Palmer | Julia Bonn | Katie Conger | James Gung
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation
Claire Bonial | Bianca Badarau | Kira Griffitt | Ulf Hermjakob | Kevin Knight | Tim O’Gorman | Martha Palmer | Nathan Schneider
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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SemEval 2018 Task 6: Parsing Time Normalizations
Egoitz Laparra | Dongfang Xu | Ahmed Elsayed | Steven Bethard | Martha Palmer
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper presents the outcomes of the Parsing Time Normalization shared task held within SemEval-2018. The aim of the task is to parse time expressions into the compositional semantic graphs of the Semantically Compositional Annotation of Time Expressions (SCATE) schema, which allows the representation of a wider variety of time expressions than previous approaches. Two tracks were included, one to evaluate the parsing of individual components of the produced graphs, in a classic information extraction way, and another one to evaluate the quality of the time intervals resulting from the interpretation of those graphs. Though 40 participants registered for the task, only one team submitted output, achieving 0.55 F1 in Track 1 (parsing) and 0.70 F1 in Track 2 (intervals).

2017

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SemEval-2017 Task 12: Clinical TempEval
Steven Bethard | Guergana Savova | Martha Palmer | James Pustejovsky
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)? Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were evaluated on clinical and pathology notes from Mayo Clinic cancer patients, annotated with an extension of TimeML for the clinical domain. 11 teams participated in the tasks, with the best systems achieving F1 scores above 0.55 for time expressions, above 0.70 for event expressions, and above 0.40 for temporal relations. Most tasks observed about a 20 point drop over Clinical TempEval 2016, where systems were trained and evaluated on the same domain (colon cancer).

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Unsupervised AMR-Dependency Parse Alignment
Wei-Te Chen | Martha Palmer
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In this paper, we introduce an Abstract Meaning Representation (AMR) to Dependency Parse aligner. Alignment is a preliminary step for AMR parsing, and our aligner improves current AMR parser performance. Our aligner involves several different features, including named entity tags and semantic role labels, and uses Expectation-Maximization training. Results show that our aligner reaches an 87.1% F-Score score with the experimental data, and enhances AMR parsing.

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Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Martha Palmer | Rebecca Hwa | Sebastian Riedel
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the Events and Stories in the News Workshop
Tommaso Caselli | Ben Miller | Marieke van Erp | Piek Vossen | Martha Palmer | Eduard Hovy | Teruko Mitamura | David Caswell
Proceedings of the Events and Stories in the News Workshop

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The Rich Event Ontology
Susan Brown | Claire Bonial | Leo Obrst | Martha Palmer
Proceedings of the Events and Stories in the News Workshop

In this paper we describe a new lexical semantic resource, The Rich Event On-tology, which provides an independent conceptual backbone to unify existing semantic role labeling (SRL) schemas and augment them with event-to-event causal and temporal relations. By unifying the FrameNet, VerbNet, Automatic Content Extraction, and Rich Entities, Relations and Events resources, the ontology serves as a shared hub for the disparate annotation schemas and therefore enables the combination of SRL training data into a larger, more diverse corpus. By adding temporal and causal relational information not found in any of the independent resources, the ontology facilitates reasoning on and across documents, revealing relationships between events that come together in temporal and causal chains to build more complex scenarios. We envision the open resource serving as a valuable tool for both moving from the ontology to text to query for event types and scenarios of interest, and for moving from text to the ontology to access interpretations of events using the combined semantic information housed there.

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Towards Problem Solving Agents that Communicate and Learn
Anjali Narayan-Chen | Colin Graber | Mayukh Das | Md Rakibul Islam | Soham Dan | Sriraam Natarajan | Janardhan Rao Doppa | Julia Hockenmaier | Martha Palmer | Dan Roth
Proceedings of the First Workshop on Language Grounding for Robotics

Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI. These agents need to translate between utterances and actionable meaning representations that can be interpreted by task-specific problem solvers in a context-dependent manner. They should also be able to learn such actionable interpretations for new predicates on the fly. We define an agent architecture for this scenario and present a series of experiments in the Blocks World domain that illustrate how our architecture supports language learning and problem solving in this domain.

2016

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Proceedings of the Fourth Workshop on Events
Martha Palmer | Ed Hovy | Teruko Mitamura | Tim O’Gorman
Proceedings of the Fourth Workshop on Events

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Multimodal Use of an Upper-Level Event Ontology
Claire Bonial | David Tahmoush | Susan Windisch Brown | Martha Palmer
Proceedings of the Fourth Workshop on Events

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A Comparison of Event Representations in DEFT
Ann Bies | Zhiyi Song | Jeremy Getman | Joe Ellis | Justin Mott | Stephanie Strassel | Martha Palmer | Teruko Mitamura | Marjorie Freedman | Heng Ji | Tim O’Gorman
Proceedings of the Fourth Workshop on Events

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Building a Cross-document Event-Event Relation Corpus
Yu Hong | Tongtao Zhang | Tim O’Gorman | Sharone Horowit-Hendler | Heng Ji | Martha Palmer
Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016)

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A Corpus of Preposition Supersenses
Nathan Schneider | Jena D. Hwang | Vivek Srikumar | Meredith Green | Abhijit Suresh | Kathryn Conger | Tim O’Gorman | Martha Palmer
Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016)

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Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation
Tim O’Gorman | Kristin Wright-Bettner | Martha Palmer
Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)

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Identifying and Categorizing Disaster-Related Tweets
Kevin Stowe | Michael J. Paul | Martha Palmer | Leysia Palen | Kenneth Anderson
Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media

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Leveraging VerbNet to build Corpus-Specific Verb Clusters
Daniel Peterson | Jordan Boyd-Graber | Martha Palmer | Daisuke Kawahara
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

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Linguistic features for Hindi light verb construction identification
Ashwini Vaidya | Sumeet Agarwal | Martha Palmer
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Light verb constructions (LVC) in Hindi are highly productive. If we can distinguish a case such as nirnay lenaa ‘decision take; decide’ from an ordinary verb-argument combination kaagaz lenaa ‘paper take; take (a) paper’,it has been shown to aid NLP applications such as parsing (Begum et al., 2011) and machine translation (Pal et al., 2011). In this paper, we propose an LVC identification system using language specific features for Hindi which shows an improvement over previous work(Begum et al., 2011). To build our system, we carry out a linguistic analysis of Hindi LVCs using Hindi Treebank annotations and propose two new features that are aimed at capturing the diversity of Hindi LVCs in the corpus. We find that our model performs robustly across a diverse range of LVCs and our results underscore the importance of semantic features, which is in keeping with the findings for English. Our error analysis also demonstrates that our classifier can be used to further refine LVC annotations in the Hindi Treebank and make them more consistent across the board.

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Large Multi-lingual, Multi-level and Multi-genre Annotation Corpus
Xuansong Li | Martha Palmer | Nianwen Xue | Lance Ramshaw | Mohamed Maamouri | Ann Bies | Kathryn Conger | Stephen Grimes | Stephanie Strassel
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

High accuracy for automated translation and information retrieval calls for linguistic annotations at various language levels. The plethora of informal internet content sparked the demand for porting state-of-art natural language processing (NLP) applications to new social media as well as diverse language adaptation. Effort launched by the BOLT (Broad Operational Language Translation) program at DARPA (Defense Advanced Research Projects Agency) successfully addressed the internet information with enhanced NLP systems. BOLT aims for automated translation and linguistic analysis for informal genres of text and speech in online and in-person communication. As a part of this program, the Linguistic Data Consortium (LDC) developed valuable linguistic resources in support of the training and evaluation of such new technologies. This paper focuses on methodologies, infrastructure, and procedure for developing linguistic annotation at various language levels, including Treebank (TB), word alignment (WA), PropBank (PB), and co-reference (CoRef). Inspired by the OntoNotes approach with adaptations to the tasks to reflect the goals and scope of the BOLT project, this effort has introduced more annotation types of informal and free-style genres in English, Chinese and Egyptian Arabic. The corpus produced is by far the largest multi-lingual, multi-level and multi-genre annotation corpus of informal text and speech.

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A Proposition Bank of Urdu
Maaz Anwar | Riyaz Ahmad Bhat | Dipti Sharma | Ashwini Vaidya | Martha Palmer | Tafseer Ahmed Khan
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper describes our efforts for the development of a Proposition Bank for Urdu, an Indo-Aryan language. Our primary goal is the labeling of syntactic nodes in the existing Urdu dependency Treebank with specific argument labels. In essence, it involves annotation of predicate argument structures of both simple and complex predicates in the Treebank corpus. We describe the overall process of building the PropBank of Urdu. We discuss various statistics pertaining to the Urdu PropBank and the issues which the annotators encountered while developing the PropBank. We also discuss how these challenges were addressed to successfully expand the PropBank corpus. While reporting the Inter-annotator agreement between the two annotators, we show that the annotators share similar understanding of the annotation guidelines and of the linguistic phenomena present in the language. The present size of this Propbank is around 180,000 tokens which is double-propbanked by the two annotators for simple predicates. Another 100,000 tokens have been annotated for complex predicates of Urdu.

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Comprehensive and Consistent PropBank Light Verb Annotation
Claire Bonial | Martha Palmer
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Recent efforts have focused on expanding the annotation coverage of PropBank from verb relations to adjective and noun relations, as well as light verb constructions (e.g., make an offer, take a bath). While each new relation type has presented unique annotation challenges, ensuring consistent and comprehensive annotation of light verb constructions has proved particularly challenging, given that light verb constructions are semi-productive, difficult to define, and there are often borderline cases. This research describes the iterative process of developing PropBank annotation guidelines for light verb constructions, the current guidelines, and a comparison to related resources.

2015

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Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics
Martha Palmer | Gemma Boleda | Paolo Rosso
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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Identification of Caused Motion Construction
Jena D. Hwang | Martha Palmer
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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Can Selectional Preferences Help Automatic Semantic Role Labeling?
Shumin Wu | Martha Palmer
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation
Eduard Hovy | Teruko Mitamura | Martha Palmer
Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation

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Improving Chinese-English PropBank Alignment
Shumin Wu | Martha Palmer
Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation

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A Hierarchy with, of, and for Preposition Supersenses
Nathan Schneider | Vivek Srikumar | Jena D. Hwang | Martha Palmer
Proceedings of The 9th Linguistic Annotation Workshop

2014

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Temporal Annotation in the Clinical Domain
William F. Styler IV | Steven Bethard | Sean Finan | Martha Palmer | Sameer Pradhan | Piet C de Groen | Brad Erickson | Timothy Miller | Chen Lin | Guergana Savova | James Pustejovsky
Transactions of the Association for Computational Linguistics, Volume 2

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.

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Inducing Example-based Semantic Frames from a Massive Amount of Verb Uses
Daisuke Kawahara | Daniel Peterson | Octavian Popescu | Martha Palmer
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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PropBank: Semantics of New Predicate Types
Claire Bonial | Julia Bonn | Kathryn Conger | Jena D. Hwang | Martha Palmer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This research focuses on expanding PropBank, a corpus annotated with predicate argument structures, with new predicate types; namely, noun, adjective and complex predicates, such as Light Verb Constructions. This effort is in part inspired by a sister project to PropBank, the Abstract Meaning Representation project, which also attempts to capture “who is doing what to whom” in a sentence, but does so in a way that abstracts away from syntactic structures. For example, alternate realizations of a ‘destroying’ event in the form of either the verb ‘destroy’ or the noun ‘destruction’ would receive the same Abstract Meaning Representation. In order for PropBank to reach the same level of coverage and continue to serve as the bedrock for Abstract Meaning Representation, predicate types other than verbs, which have previously gone without annotation, must be annotated. This research describes the challenges therein, including the development of new annotation practices that walk the line between abstracting away from language-particular syntactic facts to explore deeper semantics, and maintaining the connection between semantics and syntactic structures that has proven to be very valuable for PropBank as a corpus of training data for Natural Language Processing applications.

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Not an Interlingua, But Close: Comparison of English AMRs to Chinese and Czech
Nianwen Xue | Ondřej Bojar | Jan Hajič | Martha Palmer | Zdeňka Urešová | Xiuhong Zhang
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Abstract Meaning Representations (AMRs) are rooted, directional and labeled graphs that abstract away from morpho-syntactic idiosyncrasies such as word category (verbs and nouns), word order, and function words (determiners, some prepositions). Because these syntactic idiosyncrasies account for many of the cross-lingual differences, it would be interesting to see if this representation can serve, e.g., as a useful, minimally divergent transfer layer in machine translation. To answer this question, we have translated 100 English sentences that have existing AMRs into Chinese and Czech to create AMRs for them. A cross-linguistic comparison of English to Chinese and Czech AMRs reveals both cases where the AMRs for the language pairs align well structurally and cases of linguistic divergence. We found that the level of compatibility of AMR between English and Chinese is higher than between English and Czech. We believe this kind of comparison is beneficial to further refining the annotation standards for each of the three languages and will lead to more compatible annotation guidelines between the languages.

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Criteria for Identifying and Annotating Caused Motion Constructions in Corpus Data
Jena D. Hwang | Annie Zaenen | Martha Palmer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

While natural language processing performance has been improved through the recognition that there is a relationship between the semantics of the verb and the syntactic context in which the verb is realized, sentences where the verb does not conform to the expected syntax-semantic patterning behavior remain problematic. For example, in the sentence “The crowed laughed the clown off the stage”, a verb of non-verbal communication laugh is used in a caused motion construction and gains a motion entailment that is atypical given its inherent lexical semantics. This paper focuses on our efforts at defining the semantic types and varieties of caused motion constructions (CMCs) through an iterative annotation process and establishing annotation guidelines based on these criteria to aid in the production of a consistent and reliable annotation. The annotation will serve as training and test data for classifiers for CMCs, and the CMC definitions developed throughout this study will be used in extending VerbNet to handle representations of sentences in which a verb is used in a syntactic context that is atypical for its lexical semantics.

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Mapping CPA Patterns onto OntoNotes Senses
Octavian Popescu | Martha Palmer | Patrick Hanks
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we present an alignment experiment between patterns of verb use discovered by Corpus Pattern Analysis (CPA; Hanks 2004, 2008, 2012) and verb senses in OntoNotes (ON; Hovy et al. 2006, Weischedel et al. 2011). We present a probabilistic approach for mapping one resource into the other. Firstly we introduce a basic model, based on conditional probabilities, which determines for any given sentence the best CPA pattern match. On the basis of this model, we propose a joint source channel model (JSCM) that computes the probability of compatibility of semantic types between a verb phrase and a pattern, irrespective of whether the verb phrase is a norm or an exploitation. We evaluate the accuracy of the proposed mapping using cluster similarity metrics based on entropy.

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Single Classifier Approach for Verb Sense Disambiguation based on Generalized Features
Daisuke Kawahara | Martha Palmer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present a supervised method for verb sense disambiguation based on VerbNet. Most previous supervised approaches to verb sense disambiguation create a classifier for each verb that reaches a frequency threshold. These methods, however, have a significant practical problem that they cannot be applied to rare or unseen verbs. In order to overcome this problem, we create a single classifier to be applied to rare or unseen verbs in a new text. This single classifier also exploits generalized semantic features of a verb and its modifiers in order to better deal with rare or unseen verbs. Our experimental results show that the proposed method achieves equivalent performance to per-verb classifiers, which cannot be applied to unseen verbs. Our classifier could be utilized to improve the classifications in lexical resources of verbs, such as VerbNet, in a semi-automatic manner and to possibly extend the coverage of these resources to new verbs.

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Focusing Annotation for Semantic Role Labeling
Daniel Peterson | Martha Palmer | Shumin Wu
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Annotation of data is a time-consuming process, but necessary for many state-of-the-art solutions to NLP tasks, including semantic role labeling (SRL). In this paper, we show that language models may be used to select sentences that are more useful to annotate. We simulate a situation where only a portion of the available data can be annotated, and compare language model based selection against a more typical baseline of randomly selected data. The data is ordered using an off-the-shelf language modeling toolkit. We show that the least probable sentences provide dramatic improved system performance over the baseline, especially when only a small portion of the data is annotated. In fact, the lion’s share of the performance can be attained by annotating only 10-20% of the data. This result holds for training a model based on new annotation, as well as when adding domain-specific annotation to a general corpus for domain adaptation.

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A Step-wise Usage-based Method for Inducing Polysemy-aware Verb Classes
Daisuke Kawahara | Daniel W. Peterson | Martha Palmer
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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The VerbCorner Project: Findings from Phase 1 of crowd-sourcing a semantic decomposition of verbs
Joshua K. Hartshorne | Claire Bonial | Martha Palmer
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Proceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL)
Oleksandr Kolomiyets | Marie-Francine Moens | Martha Palmer | James Pustejovsky | Steven Bethard
Proceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL)

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An Approach to Take Multi-Word Expressions
Claire Bonial | Meredith Green | Jenette Preciado | Martha Palmer
Proceedings of the 10th Workshop on Multiword Expressions (MWE)

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Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation
Teruko Mitamura | Eduard Hovy | Martha Palmer
Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation

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Challenges of Adding Causation to Richer Event Descriptions
Rei Ikuta | Will Styler | Mariah Hamang | Tim O’Gorman | Martha Palmer
Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation

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SemLink+: FrameNet, VerbNet and Event Ontologies
Martha Palmer | Claire Bonial | Diana McCarthy
Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore (1929-2014)

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Adapting Predicate Frames for Urdu PropBanking
Riyaz Ahmad Bhat | Naman Jain | Ashwini Vaidya | Martha Palmer | Tafseer Ahmed Khan | Dipti Misra Sharma | James Babani
Proceedings of the EMNLP’2014 Workshop on Language Technology for Closely Related Languages and Language Variants

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Light verb constructions with ‘do’ and ‘be’ in Hindi: A TAG analysis
Ashwini Vaidya | Owen Rambow | Martha Palmer
Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing

2013

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Semantic Role Labeling
Martha Palmer | Ivan Titov | Shumin Wu
NAACL HLT 2013 Tutorial Abstracts

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Complex Predicates are Multi-Word Expressions
Martha Palmer
Proceedings of the 9th Workshop on Multiword Expressions

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Semantic Roles for Nominal Predicates: Building a Lexical Resource
Ashwini Vaidya | Martha Palmer | Bhuvana Narasimhan
Proceedings of the 9th Workshop on Multiword Expressions

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Workshop on Events: Definition, Detection, Coreference, and Representation
Eduard Hovy | Teruko Mitamura | Martha Palmer
Workshop on Events: Definition, Detection, Coreference, and Representation

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Abstract Meaning Representation for Sembanking
Laura Banarescu | Claire Bonial | Shu Cai | Madalina Georgescu | Kira Griffitt | Ulf Hermjakob | Kevin Knight | Philipp Koehn | Martha Palmer | Nathan Schneider
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

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Expanding VerbNet with Sketch Engine
Claire Bonial | Orin Hargraves | Martha Palmer
Proceedings of the 6th International Conference on Generative Approaches to the Lexicon (GL2013)

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Renewing and Revising SemLink
Claire Bonial | Kevin Stowe | Martha Palmer
Proceedings of the 2nd Workshop on Linked Data in Linguistics (LDL-2013): Representing and linking lexicons, terminologies and other language data

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The VerbCorner Project: Toward an Empirically-Based Semantic Decomposition of Verbs
Joshua K. Hartshorne | Claire Bonial | Martha Palmer
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Empty Argument Insertion in the Hindi PropBank
Ashwini Vaidya | Jinho D. Choi | Martha Palmer | Bhuvana Narasimhan
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper examines both linguistic behavior and practical implication of empty argument insertion in the Hindi PropBank. The Hindi PropBank is annotated on the Hindi Dependency Treebank, which contains some empty categories but not the empty arguments of verbs. In this paper, we analyze four kinds of empty arguments, *PRO*, *REL*, *GAP*, *pro*, and suggest effective ways of annotating these arguments. Empty arguments such as *PRO* and *REL* can be inserted deterministically; we present linguistically motivated rules that automatically insert these arguments with high accuracy. On the other hand, it is difficult to find deterministic rules to insert *GAP* and *pro*; for these arguments, we introduce a new annotation scheme that concurrently handles both semantic role labeling and empty category insertion, producing fast and high quality annotation. In addition, we present algorithms for finding antecedents of *REL* and *PRO*, and discuss why finding antecedents for some types of *PRO* is difficult.

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Foundations of a Multilayer Annotation Framework for Twitter Communications During Crisis Events
William J. Corvey | Sudha Verma | Sarah Vieweg | Martha Palmer | James H. Martin
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually collect and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed efficiently and effectively. We describe a natural language processing component of the EPIC (Empowering the Public with Information in Crisis) Project infrastructure, designed to extract linguistic and behavioral information from tweet text to aid in the task of information integration. The system incorporates linguistic annotation, in the form of Named Entity Tagging, as well as behavioral annotations to capture tweets contributing to situational awareness and analyze the information type of the tweet content. We show classification results and describe future integration of these classifiers in the larger EPIC infrastructure.

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Question Ranking and Selection in Tutorial Dialogues
Lee Becker | Martha Palmer | Sarel van Vuuren | Wayne Ward
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Verb Classification using Distributional Similarity in Syntactic and Semantic Structures
Danilo Croce | Alessandro Moschitti | Roberto Basili | Martha Palmer
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection
Jinho D. Choi | Martha Palmer
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Good Seed Makes a Good Crop: Accelerating Active Learning Using Language Modeling
Dmitriy Dligach | Martha Palmer
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Getting the Most out of Transition-based Dependency Parsing
Jinho D. Choi | Martha Palmer
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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VerbNet Class Assignment as a WSD Task
Susan Windisch Brown | Dmitriy Dligach | Martha Palmer
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

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DISCUSS: A dialogue move taxonomy layered over semantic representations
Lee Becker | Wayne Ward | Sarel van Vuuren | Martha Palmer
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

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Analysis of the Hindi Proposition Bank using Dependency Structure
Ashwini Vaidya | Jinho Choi | Martha Palmer | Bhuvana Narasimhan
Proceedings of the 5th Linguistic Annotation Workshop

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Reducing the Need for Double Annotation
Dmitriy Dligach | Martha Palmer
Proceedings of the 5th Linguistic Annotation Workshop

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A scaleable automated quality assurance technique for semantic representations and proposition banks
K. Bretonnel Cohen | Lawrence Hunter | Martha Palmer
Proceedings of the 5th Linguistic Annotation Workshop

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Going Beyond Shallow Semantics
Martha Palmer
Proceedings of the ACL 2011 Workshop on Relational Models of Semantics

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Transition-based Semantic Role Labeling Using Predicate Argument Clustering
Jinho D. Choi | Martha Palmer
Proceedings of the ACL 2011 Workshop on Relational Models of Semantics

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Incorporating Coercive Constructions into a Verb Lexicon
Claire Bonial | Susan Windisch Brown | Jena D. Hwang | Christopher Parisien | Martha Palmer | Suzanne Stevenson
Proceedings of the ACL 2011 Workshop on Relational Models of Semantics

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Semantic Mapping Using Automatic Word Alignment and Semantic Role Labeling
Shumin Wu | Martha Palmer
Proceedings of Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation

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CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes
Sameer Pradhan | Lance Ramshaw | Mitchell Marcus | Martha Palmer | Ralph Weischedel | Nianwen Xue
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

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Statistical Dependency Parsing in Korean: From Corpus Generation To Automatic Parsing
Jinho D. Choi | Martha Palmer
Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages

2010

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SemEval-2010 Task 10: Linking Events and Their Participants in Discourse
Josef Ruppenhofer | Caroline Sporleder | Roser Morante | Collin Baker | Martha Palmer
Proceedings of the 5th International Workshop on Semantic Evaluation

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Detecting Cross-lingual Semantic Similarity Using Parallel PropBanks
Shumin Wu | Jinho Choi | Martha Palmer
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

This paper suggests a method for detecting cross-lingual semantic similarity using parallel PropBanks. We begin by improving word alignments for verb predicates generated by GIZA++ by using information available in parallel PropBanks. We applied the Kuhn-Munkres method to measure predicate-argument matching and improved verb predicate alignments by an F-score of 12.6%. Using the enhanced word alignments we checked the set of target verbs aligned to a specific source verb for semantic consistency. For a set of English verbs aligned to a Chinese verb, we checked if the English verbs belong to the same semantic class using an existing lexical database, WordNet. For a set of Chinese verbs aligned to an English verb we manually checked semantic similarity between the Chinese verbs within a set. Our results show that the verb sets we generated have a high correlation with semantic classes. This could potentially lead to an automatic technique for generating semantic classes for verbs.

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Propbank Frameset Annotation Guidelines Using a Dedicated Editor, Cornerstone
Jinho D. Choi | Claire Bonial | Martha Palmer
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper gives guidelines of how to create and update Propbank frameset files using a dedicated editor, Cornerstone. Propbank is a corpus in which the arguments of each verb predicate are annotated with their semantic roles in relation to the predicate. Propbank annotation also requires the choice of a sense ID for each predicate. Thus, for each predicate in Propbank, there exists a corresponding frameset file showing the expected predicate argument structure of each sense related to the predicate. Since most Propbank annotations are based on the predicate argument structure defined in the frameset files, it is important to keep the files consistent, simple to read as well as easy to update. The frameset files are written in XML, which can be difficult to edit when using a simple text editor. Therefore, it is helpful to develop a user-friendly editor such as Cornerstone, specifically customized to create and edit frameset files. Cornerstone runs platform independently, is light enough to run as an X11 application and supports multiple languages such as Arabic, Chinese, English, Hindi and Korean.

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Empty Categories in a Hindi Treebank
Archna Bhatia | Rajesh Bhatt | Bhuvana Narasimhan | Martha Palmer | Owen Rambow | Dipti Misra Sharma | Michael Tepper | Ashwini Vaidya | Fei Xia
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We are in the process of creating a multi-representational and multi-layered treebank for Hindi/Urdu (Palmer et al., 2009), which has three main layers: dependency structure, predicate-argument structure (PropBank), and phrase structure. This paper discusses an important issue in treebank design which is often neglected: the use of empty categories (ECs). All three levels of representation make use of ECs. We make a high-level distinction between two types of ECs, trace and silent, on the basis of whether they are postulated to mark displacement or not. Each type is further refined into several subtypes based on the underlying linguistic phenomena which the ECs are introduced to handle. This paper discusses the stages at which we add ECs to the Hindi/Urdu treebank and why. We investigate methodically the different types of ECs and their role in our syntactic and semantic representations. We also examine our decisions whether or not to coindex each type of ECs with other elements in the representation.

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Propbank Instance Annotation Guidelines Using a Dedicated Editor, Jubilee
Jinho D. Choi | Claire Bonial | Martha Palmer
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper gives guidelines of how to annotate Propbank instances using a dedicated editor, Jubilee. Propbank is a corpus in which the arguments of each verb predicate are annotated with their semantic roles in relation to the predicate. Propbank annotation also requires the choice of a sense ID for each predicate. Jubilee facilitates this annotation process by displaying several resources of syntactic and semantic information simultaneously: the syntactic structure of a sentence is displayed in the main frame, the available senses with their corresponding argument structures are displayed in another frame, all available Propbank arguments are displayed for the annotators choice, and example annotations of each sense of the predicate are available to the annotator for viewing. Easy access to each of these resources allows the annotator to quickly absorb and apply the necessary syntactic and semantic information pertinent to each predicate for consistent and efficient annotation. Jubilee has been successfully adapted to many Propbank projects in several universities. The tool runs platform independently, is light enough to run as an X11 application and supports multiple languages such as Arabic, Chinese, English, Hindi and Korean.

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Number or Nuance: Which Factors Restrict Reliable Word Sense Annotation?
Susan Windisch Brown | Travis Rood | Martha Palmer
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This study attempts to pinpoint the factors that restrict reliable word sense annotation, focusing on the influence of the number of senses annotators use and the semantic granularity of those senses. Both of these factors may be possible causes of low interannotator agreement (ITA) when tagging with fine-grained word senses, and, consequently, low WSD system performance (Ng et al., 1999; Snyder & Palmer, 2004; Chklovski & Mihalcea, 2002). If number of senses is the culprit, modifying the task to show fewer senses at a time could improve annotator reliability. However, if overly nuanced distinctions are the problem, then more general, coarse-grained distinctions may be necessary for annotator success and may be all that is needed to supply systems with the types of distinctions that people make. We describe three experiments that explore the role of these factors in annotation performance. Our results indicate that of these two factors, only the granularity of the senses restricts interannotator agreement, with broader senses resulting in higher annotation reliability.

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A Road Map for Interoperable Language Resource Metadata
Christopher Cieri | Khalid Choukri | Nicoletta Calzolari | D. Terence Langendoen | Johannes Leveling | Martha Palmer | Nancy Ide | James Pustejovsky
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

LRs remain expensive to create and thus rare relative to demand across languages and technology types. The accidental re-creation of an LR that already exists is a nearly unforgivable waste of scarce resources that is unfortunately not so easy to avoid. The number of catalogs the HLT researcher must search, with their different formats, make it possible to overlook an existing resource. This paper sketches the sources of this problem and outlines a proposal to rectify along with a new vision of LR cataloging that will to facilitates the documentation and exploitation of a much wider range of LRs than previously considered.

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Twitter in Mass Emergency: What NLP Can Contribute
William J. Corvey | Sarah Vieweg | Travis Rood | Martha Palmer
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media

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Towards a Domain Independent Semantics: Enhancing Semantic Representation with Construction Grammar
Jena D. Hwang | Rodney D. Nielsen | Martha Palmer
Proceedings of the NAACL HLT Workshop on Extracting and Using Constructions in Computational Linguistics

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To Annotate More Accurately or to Annotate More
Dmitriy Dligach | Rodney Nielsen | Martha Palmer
Proceedings of the Fourth Linguistic Annotation Workshop

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PropBank Annotation of Multilingual Light Verb Constructions
Jena D. Hwang | Archna Bhatia | Claire Bonial | Aous Mansouri | Ashwini Vaidya | Nianwen Xue | Martha Palmer
Proceedings of the Fourth Linguistic Annotation Workshop

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Retrieving Correct Semantic Boundaries in Dependency Structure
Jinho Choi | Martha Palmer
Proceedings of the Fourth Linguistic Annotation Workshop

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An Overview of the CRAFT Concept Annotation Guidelines
Michael Bada | Miriam Eckert | Martha Palmer | Lawrence Hunter
Proceedings of the Fourth Linguistic Annotation Workshop

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The Revised Arabic PropBank
Wajdi Zaghouani | Mona Diab | Aous Mansouri | Sameer Pradhan | Martha Palmer
Proceedings of the Fourth Linguistic Annotation Workshop

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Multilingual Propbank Annotation Tools: Cornerstone and Jubilee
Jinho Choi | Claire Bonial | Martha Palmer
Proceedings of the NAACL HLT 2010 Demonstration Session

2009

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Using Language Modeling to Select Useful Annotation Data
Dmitriy Dligach | Martha Palmer
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

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SemEval-2010 Task 10: Linking Events and Their Participants in Discourse
Josef Ruppenhofer | Caroline Sporleder | Roser Morante | Collin Baker | Martha Palmer
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009)

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Using Parallel Propbanks to enhance Word-alignments
Jinho Choi | Martha Palmer | Nianwen Xue
Proceedings of the Third Linguistic Annotation Workshop (LAW III)

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A Multi-Representational and Multi-Layered Treebank for Hindi/Urdu
Rajesh Bhatt | Bhuvana Narasimhan | Martha Palmer | Owen Rambow | Dipti Sharma | Fei Xia
Proceedings of the Third Linguistic Annotation Workshop (LAW III)

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Knowing a word (sense) by its company
Martha Palmer
Proceedings of the Eight International Conference on Computational Semantics

2008

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Annotating Students’ Understanding of Science Concepts
Rodney D. Nielsen | Wayne Ward | James Martin | Martha Palmer
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper summarizes the annotation of fine-grained entailment relationships in the context of student answers to science assessment questions. We annotated a corpus of 15,357 answer pairs with 145,911 fine-grained entailment relationships. We provide the rationale for such fine-grained analysis and discuss its perceived benefits to an Intelligent Tutoring System. The corpus also has potential applications in other areas, such as question answering and multi-document summarization. Annotators achieved 86.2% inter-annotator agreement (Kappa=0.728, corresponding to substantial agreement) annotating the fine-grained facets of reference answers with regard to understanding expressed in student answers and labeling from one of five possible detailed relationship categories. The corpus described in this paper, which is the only one providing such detailed entailment annotations, is available as a public resource for the research community. The corpus is expected to enable application development, not only for intelligent tutoring systems, but also for general textual entailment applications, that is currently not practical.

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A Pilot Arabic Propbank
Martha Palmer | Olga Babko-Malaya | Ann Bies | Mona Diab | Mohamed Maamouri | Aous Mansouri | Wajdi Zaghouani
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper, we present the details of creating a pilot Arabic proposition bank (Propbank). Propbanks exist for both English and Chinese. However the morphological and syntactic expression of linguistic phenomena in Arabic yields a very different type of process in creating an Arabic propbank. Hence, we highlight those characteristics of Arabic that make creating a propbank for the language a different challenge compared to the creation of an English Propbank.We believe that many of the lessons learned in dealing with Arabic could generalise to other languages that exhibit equally rich morphology and relatively free word order.

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Novel Semantic Features for Verb Sense Disambiguation
Dmitriy Dligach | Martha Palmer
Proceedings of ACL-08: HLT, Short Papers

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Extracting a Representation from Text for Semantic Analysis
Rodney D. Nielsen | Wayne Ward | James H. Martin | Martha Palmer
Proceedings of ACL-08: HLT, Short Papers

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Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics
Martha Palmer | Chris Brew | Fei Xia
Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics

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Invited Talk: The Relevance of a Cognitive Model of the Mental Lexicon to Automatic Word Sense Disambiguation
Martha Palmer | Susan Brown
Coling 2008: Proceedings of the workshop on Human Judgements in Computational Linguistics

2007

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SemEval-2007 Task-17: English Lexical Sample, SRL and All Words
Sameer Pradhan | Edward Loper | Dmitriy Dligach | Martha Palmer
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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SemEval-2007 Task 18: Arabic Semantic Labeling
Mona Diab | Musa Alkhalifa | Sabry ElKateb | Christiane Fellbaum | Aous Mansouri | Martha Palmer
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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Criteria for the Manual Grouping of Verb Senses
Cecily Jill Duffield | Jena D. Hwang | Susan Windisch Brown | Dmitriy Dligach | Sarah E. Vieweg | Jenny Davis | Martha Palmer
Proceedings of the Linguistic Annotation Workshop

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Can Semantic Roles Generalize Across Genres?
Szu-ting Yi | Edward Loper | Martha Palmer
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

2006

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Better Learning and Decoding for Syntax Based SMT Using PSDIG
Yuan Ding | Martha Palmer
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers

As an approach to syntax based statistical machine translation (SMT), Probabilistic Synchronous Dependency Insertion Grammars (PSDIG), introduced in (Ding and Palmer, 2005), are a version of synchronous grammars defined on dependency trees. In this paper we discuss better learning and decoding algorithms for a PSDIG MT system. We introduce two new grammar learners: (1) an exhaustive learner combining different heuristics, (2) an n-gram based grammar learner. Combining the grammar rules learned from the two learners improved the performance. We introduce a better decoding algorithm which incorporates a tri-gram language model. According to the Bleu metric, the PSDIG MT system performance is significantly better than IBM Model 4, while on par with the state-of-the-art phrase based system Pharaoh (Koehn, 2004). The improved integration of syntax on both source and target languages opens door to more sophisticated SMT processes.

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Extending VerbNet with Novel Verb Classes
Karin Kipper | Anna Korhonen | Neville Ryant | Martha Palmer
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Lexical classifications have proved useful in supporting various natural language processing (NLP) tasks. The largest verb classification for English is Levin's (1993) work which defined groupings of verbs based on syntactic properties. VerbNet - the largest computational verb lexicon currently available for English - provides detailed syntactic-semantic descriptions of Levin classes. While the classes included are extensive enough for some NLP use, they are not comprehensive. Korhonen and Briscoe (2004) have proposed a significant extension of Levin's classification which incorporates 57 novel classes for verbs not covered (comprehensively) by Levin. This paper describes the integration of these classes into VerbNet. The result is the most extensive Levin-style classification for English verbs which can be highly useful for practical applications.

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Aligning Features with Sense Distinction Dimensions
Nianwen Xue | Jinying Chen | Martha Palmer
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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An Empirical Study of the Behavior of Active Learning for Word Sense Disambiguation
Jinying Chen | Andrew Schein | Lyle Ungar | Martha Palmer
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

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OntoNotes: The 90% Solution
Eduard Hovy | Mitchell Marcus | Martha Palmer | Lance Ramshaw | Ralph Weischedel
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

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Issues in Synchronizing the English Treebank and PropBank
Olga Babko-Malaya | Ann Bies | Ann Taylor | Szuting Yi | Martha Palmer | Mitch Marcus | Seth Kulick | Libin Shen
Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006

2005

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The Proposition Bank: An Annotated Corpus of Semantic Roles
Martha Palmer | Daniel Gildea | Paul Kingsbury
Computational Linguistics, Volume 31, Number 1, March 2005

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Merging PropBank, NomBank, TimeBank, Penn Discourse Treebank and Coreference
James Pustejovsky | Adam Meyers | Martha Palmer | Massimo Poesio
Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky

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A Parallel Proposition Bank II for Chinese and English
Martha Palmer | Nianwen Xue | Olga Babko-Malaya | Jinying Chen | Benjamin Snyder
Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky

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The Integration of Syntactic Parsing and Semantic Role Labeling
Szu-ting Yi | Martha Palmer
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

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The Role of Semantic Roles in Disambiguating Verb Senses
Hoa Trang Dang | Martha Palmer
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars
Yuan Ding | Martha Palmer
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features
Jinying Chen | Martha Palmer
Second International Joint Conference on Natural Language Processing: Full Papers

2004

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Extending a Verb-lexicon Using a Semantically Annotated Corpus
Karin Kipper | Benjamin Snyder | Martha Palmer
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Chinese Verb Sense Discrimination Using an EM Clustering Model with Rich Linguistic Features
Jinying Chen | Martha Palmer
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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The English all-words task
Benjamin Snyder | Martha Palmer
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

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Synchronous Dependency Insertion Grammars: A Grammar Formalism for Syntax Based Statistical MT
Yuan Ding | Martha Palmer
Proceedings of the Workshop on Recent Advances in Dependency Grammar

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Putting Meaning into Your Trees
Martha Palmer
Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004

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Using prepositions to extend a verb lexicon
Karin Kipper | Benjamin Snyder | Martha Palmer
Proceedings of the Computational Lexical Semantics Workshop at HLT-NAACL 2004

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Proposition Bank II: Delving Deeper
Olga Babko-Malaya | Martha Palmer | Nianwen Xue | Aravind Joshi | Seth Kulick
Proceedings of the Workshop Frontiers in Corpus Annotation at HLT-NAACL 2004

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Different Sense Granularities for Different Applications
Martha Palmer | Olga Babko-Malaya | Hoa Trang Dang
Proceedings of the 2nd International Workshop on Scalable Natural Language Understanding (ScaNaLU 2004) at HLT-NAACL 2004

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Integrated Annotation for Biomedical Information Extraction
Seth Kulick | Ann Bies | Mark Liberman | Mark Mandel | Ryan McDonald | Martha Palmer | Andrew Schein | Lyle Ungar | Scott Winters | Pete White
HLT-NAACL 2004 Workshop: Linking Biological Literature, Ontologies and Databases

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Calibrating Features for Semantic Role Labeling
Nianwen Xue | Martha Palmer
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

2003

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An algorithm for word-level alignment of parallel dependency trees
Yuan Ding | Daniel Gildea | Martha Palmer
Proceedings of Machine Translation Summit IX: Papers

Structural divergence presents a challenge to the use of syntax in statistical machine translation. We address this problem with a new algorithm for alignment of loosely matched non-isomorphic dependency trees. The algorithm selectively relaxes the constraints of the two tree structures while keeping computational complexity polynomial in the length of the sentences. Experimentation with a large Chinese-English corpus shows an improvement in alignment results over the unstructured models of (Brown et al., 1993).

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Annotating the Propositions in the Penn Chinese Treebank
Nianwen Xue | Martha Palmer
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

2002

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Development and Evaluation of a Korean Treebank and its Application to NLP
Chung-hye Han | Na-Rae Han | Eon-Suk Ko | Martha Palmer
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Standards & best practice for multilingual computational lexicons: ISLE MILE and more”
Nicoletta Calzolari | Ralph Grishman | Martha Palmer
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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From Resources to Applications. Designing the Multilingual ISLE Lexical Entry
Sue Atkins | Nuria Bel | Francesca Bertagna | Pierrette Bouillon | Nicoletta Calzolari | Christiane Fellbaum | Ralph Grishman | Alessandro Lenci | Catherine MacLeod | Martha Palmer | Gregor Thurmair | Marta Villegas | Antonio Zampolli
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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From TreeBank to PropBank
Paul Kingsbury | Martha Palmer
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Simple Features for Chinese Word Sense Disambiguation
Hoa Trang Dang | Ching-yi Chia | Martha Palmer | Fu-Dong Chiou
COLING 2002: The 19th International Conference on Computational Linguistics

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Building a Large-Scale Annotated Chinese Corpus
Nianwen Xue | Fu-Dong Chiou | Martha Palmer
COLING 2002: The 19th International Conference on Computational Linguistics

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The Necessity of Parsing for Predicate Argument Recognition
Daniel Gildea | Martha Palmer
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Combining Contextual Features for Word Sense Disambiguation
Hoa Trang Dang | Martha Palmer
Proceedings of the ACL-02 Workshop on Word Sense Disambiguation: Recent Successes and Future Directions

2001

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Automatic Predicate Argument Analysis of the Penn TreeBank
Martha Palmer | Joseph Rosenzweig | Scott Cotton
Proceedings of the First International Conference on Human Language Technology Research

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Converting Dependency Structures to Phrase Structures
Fei Xia | Martha Palmer
Proceedings of the First International Conference on Human Language Technology Research

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Facilitating Treebank Annotation Using a Statistical Parser
Fu-Dong Chiou | David Chiang | Martha Palmer
Proceedings of the First International Conference on Human Language Technology Research

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English Tasks: All-Words and Verb Lexical Sample
Martha Palmer | Christiane Fellbaum | Scott Cotton | Lauren Delfs | Hoa Trang Dang
Proceedings of SENSEVAL-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems

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Penn Korean Treebank : Development and Evaluation
Chung-hye Han | Na-Rae Han | Eon-Suk Ko | Martha Palmer | Heejong Yi
Proceedings of the 16th Pacific Asia Conference on Language, Information and Computation

2000

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Semantic Tagging for the Penn Treebank
Martha Palmer | Hoa Trang Dang | Joseph Rosenzweig
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Developing Guidelines and Ensuring Consistency for Chinese Text Annotation
Fei Xia | Martha Palmer | Nianwen Xue | Mary Ellen Okurowski | John Kovarik | Fu-Dong Chiou | Shizhe Huang | Tony Kroch | Mitch Marcus
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Representations of Actions as an Interlingua
Karin Christine Kipper | Martha Palmer
NAACL-ANLP 2000 Workshop: Applied Interlinguas: Practical Applications of Interlingual Approaches to NLP

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Towards Translingual Information Access using Portable Information Extraction
Michael White | Claire Cardie | Chung-hye Han | Nari Kim | Benoit Lavoie | Martha Palmer | Owen Rainbow | Juntae Yoon
ANLP-NAACL 2000 Workshop: Embedded Machine Translation Systems

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Comparing Lexicalized Treebank Grammars Extracted from Chinese, Korean, and English Corpora
Fei Xia | Chunghye Han | Martha Palmer | Aravind Joshi
Second Chinese Language Processing Workshop

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A Uniform Method of Grammar Extraction and Its Applications
Fei Xia | Martha Palmer | Aravind Joshi
2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

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Building a class-based verb lexicon using TAGs
Karin Kipper | Hoa Trang Dang | William Schuler | Martha Palmer
Proceedings of the Fifth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+5)

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Lexicalized grammar and the description of motion events
Matthew Stone | Tonia Bleam | Christine Doran | Martha Palmer
Proceedings of the Fifth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+5)

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Comparing and integrating Tree Adjoining Grammars
Fei Xia | Martha Palmer
Proceedings of the Fifth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+5)

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Integrating compositional semantics into a verb lexicon
Hoa Trang Dang | Karin Kipper | Martha Palmer
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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Handling structural divergences and recovering dropped arguments in a Korean/English machine translation system
Chung-hye Han | Benoit Lavoie | Martha Palmer | Owen Rambow | Richard Kittredge | Tanya Korelsky | Nari Kim | Myunghee Kim
Proceedings of the Fourth Conference of the Association for Machine Translation in the Americas: Technical Papers

This paper describes an approach for handling structural divergences and recovering dropped arguments in an implemented Korean to English machine translation system. The approach relies on canonical predicate-argument structures (or dependency structures), which provide a suitable pivot representation for the handling of structural divergences and the recovery of dropped arguments. It can also be converted to and from the interface representations of many off-the-shelf parsers and generators.

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A machine translation system from English to American Sign Language
Liwei Zhao | Karin Kipper | William Schuler | Christian Vogler | Norman Badler | Martha Palmer
Proceedings of the Fourth Conference of the Association for Machine Translation in the Americas: Technical Papers

Research in computational linguistics, computer graphics and autonomous agents has led to the development of increasingly sophisticated communicative agents over the past few years, bringing new perspective to machine translation research. The engineering of language- based smooth, expressive, natural-looking human gestures can give us useful insights into the design principles that have evolved in natural communication between people. In this paper we prototype a machine translation system from English to American Sign Language (ASL), taking into account not only linguistic but also visual and spatial information associated with ASL signs.

1998

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Investigating Regular Sense Extensions based on Intersective Levin Classes
Hoa Trang Dang | Karin Kipper | Martha Palmer | Joseph Rosenzweig
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Investigating regular sense extensions based on intersective Levin classes
Hoa Trang Dang | Karin Kipper | Martha Palmer | Joseph Rosenzweig
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Motion verbs and semantic features in TAG
Tonia Bleam | Martha Palmer | K. Vijay-Shanker
Proceedings of the Fourth International Workshop on Tree Adjoining Grammars and Related Frameworks (TAG+4)

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Consistent grammar development using partial-tree descriptions for Lexicalized Tree-Adjoining Grammars
Fei Xia | Martha Palmer | K. Vijay-Shanker | Joseph Rosenzweig
Proceedings of the Fourth International Workshop on Tree Adjoining Grammars and Related Frameworks (TAG+4)

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Proceedings of the Pilot SENSEVAL
Adam Kilgarriff | Martha Palmer
Proceedings of the Pilot SENSEVAL

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Rapid prototyping of domain-apecific machine translation systems
Martha Palmer | Owen Rambow | Alexis Nasr
Proceedings of the Third Conference of the Association for Machine Translation in the Americas: Technical Papers

This paper reports on an experiment in assembling a domain-specific machine translation prototype system from off-the-shelf components. The design goals of this experiment were to reuse existing components, to use machine-learning techniques for parser specialization and for transfer lexicon extraction, and to use an expressive, lexicalized formalism for the transfer component.

1997

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Associating semantic components with intersective Levin classes
Hoa Trang Dang | Joseph Rosenzweig | Martha Palmer
AMTA/SIG-IL First Workshop on Interlinguas

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Enriching lexical transfer with cross-linguistic semantic features or how to do interlingua without interlingua
Alexis Nasr | Owen Rambow | Martha Palmer | Joseph Rosenzweig
AMTA/SIG-IL First Workshop on Interlinguas

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An English Grammar Checker as a Writing Aid for Students of English as a Second Language
Jong C. Park | Martha Palmer | Clay Washburn
Fifth Conference on Applied Natural Language Processing: Descriptions of System Demonstrations and Videos

1996

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A Statistically Emergent Approach for Language Processing: Application to Modeling Context Effects in Ambiguous Chinese Word Boundary Perception
Kok-Wee Gan | Martha Palmer | Kim-Teng Lua
Computational Linguistics, Volume 22, Number 4, December 1996

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Capturing motion verb generalizations in synchronous tree adjoining grammars
Martha Palmer | Joseph Rosenzweig
Conference of the Association for Machine Translation in the Americas

1994

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Verb Semantics and Lexical Selection
Zhibiao Wu | Martha Palmer
32nd Annual Meeting of the Association for Computational Linguistics

1993

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Developing a Chinese Module in UNITRAN
Zhibiao Wu | Loke Soo Hsu | Martha Palmer | Chew Lim Tan
ROCLING 1993 Short Papers

1991

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General Lexical Representation for an Effect Predicate
Martha Palmer
Lexical Semantics and Knowledge Representation

1990

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Workshop on the Evaluation of Natural Language Processing Systems
Martha Palmer | Tim Finin
Computational Linguistics, Volume 16, Number 3, September 1990

1989

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Natural Language Understanding: Integrating Syntax, Semantics, and Discourse.
Lynette Hirschman | Martha Palmer
Speech and Natural Language: Proceedings of a Workshop Held at Philadelphia, Pennsylvania, February 21-23, 1989

1987

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Nominalizations in PUNDIT
Deborah A. Dahl | Martha S. Palmer | Rebecca J. Passonneau
25th Annual Meeting of the Association for Computational Linguistics

1986

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Recovering Implicit Information
Martha S. Palmer | Deborah A. Dahl | Rebecca J. Schiffman | Lynette Hirschman | Marcia Linebarger | John Dowding
Strategic Computing - Natural Language Workshop: Proceedings of a Workshop Held at Marina del Rey, California, May 1-2, 1986

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Recovering Implicit Information
Martha S. Palmer | Deborah A. Dahl | Rebecca J. Schiffman | Lynette Hirschman | Marcia Linebarger | John Dowding
24th Annual Meeting of the Association for Computational Linguistics

1983

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Parsing With Logical Variables
Timothy W. Finin | Martha Stone Palmer
First Conference on Applied Natural Language Processing

1981

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A Case for Rule-Driven Semantic Processing
Martha Palmer
19th Annual Meeting of the Association for Computational Linguistics

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