Tim O’Gorman

Also published as: Tim O’gorman


2022

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DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
Neha Kennard | Tim O’Gorman | Rajarshi Das | Akshay Sharma | Chhandak Bagchi | Matthew Clinton | Pranay Kumar Yelugam | Hamed Zamani | Andrew McCallum
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined. We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. DISAPERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing their context in the review and the authors’ stance towards review arguments. Further, we annotate every review and rebuttal sentence. We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.

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DocAMR: Multi-Sentence AMR Representation and Evaluation
Tahira Naseem | Austin Blodgett | Sadhana Kumaravel | Tim O’Gorman | Young-Suk Lee | Jeffrey Flanigan | Ramón Astudillo | Radu Florian | Salim Roukos | Nathan Schneider
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Despite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation. Taking advantage of a super-sentential level of coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information loss from over-merging and lack of coherence from under merging. Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs and use it to re-evaluate the best published document-level AMR parser. We also present a pipeline approach combining the top-performing AMR parser and coreference resolution systems, providing a strong baseline for future research.

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PropBank Comes of Age—Larger, Smarter, and more Diverse
Sameer Pradhan | Julia Bonn | Skatje Myers | Kathryn Conger | Tim O’gorman | James Gung | Kristin Wright-bettner | Martha Palmer
Proceedings of the 11th Joint Conference on Lexical and Computational Semantics

This paper describes the evolution of the PropBank approach to semantic role labeling over the last two decades. During this time the PropBank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions. The number of domains, genres and languages that have been PropBanked has also expanded greatly, creating an opportunity for much more challenging and robust testing of the generalization capabilities of PropBank semantic role labeling systems. We also describe the substantial effort that has gone into ensuring the consistency and reliability of the various annotated datasets and resources, to better support the training and evaluation of such systems

2021

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MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text
Tim O’Gorman | Zach Jensen | Sheshera Mysore | Kevin Huang | Rubayyat Mahbub | Elsa Olivetti | Andrew McCallum
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials. However, a fundamental challenge in building information extraction models for material science synthesis procedures is getting accurate labels for the materials, operations, and other entities of those procedures. We present a new corpus of entity mention annotations over 595 Material Science synthesis procedural texts (157,488 tokens), which greatly expands the training data available for the Named Entity Recognition task. We outline a new label inventory designed to provide consistent annotations and a new annotation approach intended to maximize the consistency and annotation speed of domain experts. Inter-annotator agreement studies and baseline models trained upon the data suggest that the corpus provides high-quality annotations of these mention types. This corpus helps lay a foundation for future high-quality modeling of synthesis procedures.

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Improved Latent Tree Induction with Distant Supervision via Span Constraints
Zhiyang Xu | Andrew Drozdov | Jay Yoon Lee | Tim O’Gorman | Subendhu Rongali | Dylan Finkbeiner | Shilpa Suresh | Mohit Iyyer | Andrew McCallum
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this work, we present a technique that uses distant supervision in the form of span constraints (i.e. phrase bracketing) to improve performance in unsupervised constituency parsing. Using a relatively small number of span constraints we can substantially improve the output from DIORA, an already competitive unsupervised parsing system. Compared with full parse tree annotation, span constraints can be acquired with minimal effort, such as with a lexicon derived from Wikipedia, to find exact text matches. Our experiments show span constraints based on entities improves constituency parsing on English WSJ Penn Treebank by more than 5 F1. Furthermore, our method extends to any domain where span constraints are easily attainable, and as a case study we demonstrate its effectiveness by parsing biomedical text from the CRAFT dataset.

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Theoretical and Practical Issues in the Semantic Annotation of Four Indigenous Languages
Jens E. L. Van Gysel | Meagan Vigus | Lukas Denk | Andrew Cowell | Rosa Vallejos | Tim O’Gorman | William Croft
Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

Computational resources such as semantically annotated corpora can play an important role in enabling speakers of indigenous minority languages to participate in government, education, and other domains of public life in their own language. However, many languages – mainly those with small native speaker populations and without written traditions – have little to no digital support. One hurdle in creating such resources is that for many languages, few speakers would be capable of annotating texts – a task which requires literacy and some linguistic training – and that these experts’ time is typically in high demand for language planning work. This paper assesses whether typologically trained non-speakers of an indigenous language can feasibly perform semantic annotation using Uniform Meaning Representations, thus allowing for the creation of computational materials without putting further strain on community resources.

2020

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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing
Stephan Oepen | Omri Abend | Lasha Abzianidze | Johan Bos | Jan Hajic | Daniel Hershcovich | Bin Li | Tim O’Gorman | Nianwen Xue | Daniel Zeman
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu

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Cross-lingual annotation: a road map for low- and no-resource languages
Meagan Vigus | Jens E. L. Van Gysel | Tim O’Gorman | Andrew Cowell | Rosa Vallejos | William Croft
Proceedings of the Second International Workshop on Designing Meaning Representations

This paper presents a “road map” for the annotation of semantic categories in typologically diverse languages, with potentially few linguistic resources, and often no existing computational resources. Past semantic annotation efforts have focused largely on high-resource languages, or relatively low-resource languages with a large number of native speakers. However, there are certain typological traits, namely the synthesis of multiple concepts into a single word, that are more common in languages with a smaller speech community. For example, what is expressed as a sentence in a more analytic language like English, may be expressed as a single word in a more synthetic language like Arapaho. This paper proposes solutions for annotating analytic and synthetic languages in a comparable way based on existing typological research, and introduces a road map for the annotation of languages with a dearth of resources.

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An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text
Daivik Swarup | Ahsaas Bajaj | Sheshera Mysore | Tim O’Gorman | Rajarshi Das | Andrew McCallum
Findings of the Association for Computational Linguistics: EMNLP 2020

In specific domains, such as procedural scientific text, human labeled data for shallow semantic parsing is especially limited and expensive to create. Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric. In light of this opportunity, this paper explores an instance-based approach to the relation prediction sub-task within shallow semantic parsing, in which semantic labels from structurally similar sentences in the training set are copied to test sentences. Candidate similar sentences are retrieved using SciBERT embeddings. For labels where it is possible to copy from a similar sentence we employ an instance level copy network, when this is not possible, a globally shared parametric model is employed. Experiments show our approach outperforms both baseline and prior methods by 0.75 to 3 F1 absolute in the Wet Lab Protocol Corpus and 1 F1 absolute in the Materials Science Procedural Text Corpus.

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ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Michael Boratko | Xiang Li | Tim O’Gorman | Rajarshi Das | Dan Le | Andrew McCallum
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Given questions regarding some prototypical situation — such as Name something that people usually do before they leave the house for work? — a human can easily answer them via acquired experiences. There can be multiple right answers for such questions, with some more common for a situation than others. This paper introduces a new question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations. The training set is gathered from an existing set of questions played in a long-running international trivia game show – Family Feud. The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers. We also propose a generative evaluation task where a model has to output a ranked list of answers, ideally covering all prototypical answers for a question. After presenting multiple competitive baseline models, we find that human performance still exceeds model scores on all evaluation metrics with a meaningful gap, supporting the challenging nature of the task.

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Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders
Andrew Drozdov | Subendhu Rongali | Yi-Pei Chen | Tim O’Gorman | Mohit Iyyer | Andrew McCallum
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The deep inside-outside recursive autoencoder (DIORA; Drozdov et al. 2019) is a self-supervised neural model that learns to induce syntactic tree structures for input sentences *without access to labeled training data*. In this paper, we discover that while DIORA exhaustively encodes all possible binary trees of a sentence with a soft dynamic program, its vector averaging approach is locally greedy and cannot recover from errors when computing the highest scoring parse tree in bottom-up chart parsing. To fix this issue, we introduce S-DIORA, an improved variant of DIORA that encodes a single tree rather than a softly-weighted mixture of trees by employing a hard argmax operation and a beam at each cell in the chart. Our experiments show that through *fine-tuning* a pre-trained DIORA with our new algorithm, we improve the state of the art in *unsupervised* constituency parsing on the English WSJ Penn Treebank by 2.2-6% F1, depending on the data used for fine-tuning.

2019

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Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning
Stephan Oepen | Omri Abend | Jan Hajic | Daniel Hershcovich | Marco Kuhlmann | Tim O’Gorman | Nianwen Xue
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

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MRP 2019: Cross-Framework Meaning Representation Parsing
Stephan Oepen | Omri Abend | Jan Hajic | Daniel Hershcovich | Marco Kuhlmann | Tim O’Gorman | Nianwen Xue | Jayeol Chun | Milan Straka | Zdenka Uresova
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five distinct approaches to the representation of sentence meaning in the form of directed graph were represented in the training and evaluation data for the task, packaged in a uniform abstract graph representation and serialization. The task received submissions from eighteen teams, of which five do not participate in the official ranking because they arrived after the closing deadline, made use of additional training data, or involved one of the task co-organizers. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu

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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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.

2017

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Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions
Jena D. Hwang | Archna Bhatia | Na-Rae Han | Tim O’Gorman | Vivek Srikumar | Nathan Schneider
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4,250 preposition tokens in a 55,000 word corpus of English. Attempts to apply the scheme to adpositions and case markers in other languages, as well as some problematic cases in English, have led us to reconsider the assumption that an adposition’s lexical contribution is equivalent to the role/relation that it mediates. Our proposal is to embrace the potential for construal in adposition use, expressing such phenomena directly at the token level to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition’s lexical function so they can be annotated at scale—supporting automatic, statistical processing of domain-general language—and discuss how this representation would allow for a simpler inventory of labels.

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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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)

2015

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The Logic of AMR: Practical, Unified, Graph-Based Sentence Semantics for NLP
Nathan Schneider | Jeffrey Flanigan | Tim O’Gorman
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

2014

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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