Ghazaleh Kazeminejad


2023

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Event Semantic Knowledge in Procedural Text Understanding
Ghazaleh Kazeminejad | Martha Palmer
Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)

The task of entity state tracking aims to automatically analyze procedural texts – texts that describe a step-by-step process (e.g. a baking recipe). Specifically, the goal is to track various states of the entities participating in a given process. Some of the challenges for this NLP task include annotated data scarcity and annotators’ reliance on commonsense knowledge to annotate implicit state information. Zhang et al. (2021) successfully incorporated commonsense entity-centric knowledge from ConceptNet into their BERT-based neural-symbolic architecture. Since English mostly encodes state change information in verbs, we attempted to test whether injecting semantic knowledge of events (retrieved from the state-of-the-art VerbNet parser) into a neural model can also improve the performance on this task. To achieve this, we adapt the methodology introduced by Zhang et al. (2021) for incorporating symbolic entity information from ConceptNet to the incorporation of VerbNet event semantics. We evaluate the performance of our model on the ProPara dataset (Mishra et al., 2018). In addition, we introduce a purely symbolic model for entity state tracking that uses a simple set of case statements, and is informed mostly by linguistic knowledge retrieved from various computational lexical resources. Our approach is inherently domain-agnostic, and our model is explainable and achieves state-of-the-art results on the Recipes dataset (Bosselut et al., 2017).

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Learning Semantic Role Labeling from Compatible Label Sequences
Tao Li | Ghazaleh Kazeminejad | Susan Brown | Vivek Srikumar | Martha Palmer
Findings of the Association for Computational Linguistics: EMNLP 2023

Semantic role labeling (SRL) has multiple disjoint label sets, e.g., VerbNet and PropBank. Creating these datasets is challenging, therefore a natural question is how to use each one to help the other. Prior work has shown that cross-task interaction helps, but only explored multitask learning so far. A common issue with multi-task setup is that argument sequences are still separately decoded, running the risk of generating structurally inconsistent label sequences (as per lexicons like Semlink). In this paper, we eliminate such issue with a framework that jointly models VerbNet and PropBank labels as one sequence. In this setup, we show that enforcing Semlink constraints during decoding constantly improves the overall F1. With special input constructions, our joint model infers VerbNet arguments from given PropBank arguments with over 99 F1. For learning, we propose a constrained marginal model that learns with knowledge defined in Semlink to further benefit from the large amounts of PropBank-only data. On the joint benchmark based on CoNLL05, our models achieve state-of-the-art F1’s, outperforming the prior best in-domain model by 3.5 (VerbNet) and 0.8 (PropBank). For out-of-domain generalization, our models surpass the prior best by 3.4 (VerbNet) and 0.2 (PropBank).

2022

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RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
Xinya Du | Zixuan Zhang | Sha Li | Pengfei Yu | Hongwei Wang | Tuan Lai | Xudong Lin | Ziqi Wang | Iris Liu | Ben Zhou | Haoyang Wen | Manling Li | Darryl Hannan | Jie Lei | Hyounghun Kim | Rotem Dror | Haoyu Wang | Michael Regan | Qi Zeng | Qing Lyu | Charles Yu | Carl Edwards | Xiaomeng Jin | Yizhu Jiao | Ghazaleh Kazeminejad | Zhenhailong Wang | Chris Callison-Burch | Mohit Bansal | Carl Vondrick | Jiawei Han | Dan Roth | Shih-Fu Chang | Martha Palmer | Heng Ji
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios. The framework consists of two parts: (1) an open-domain end-to-end multimedia multilingual information extraction system with weak-supervision and zero-shot learningbased techniques. (2) schema matching and schema-guided event prediction based on our curated schema library. We build a demo website based on our dockerized system and schema library publicly available for installation (https://github.com/RESIN-KAIROS/RESIN-11). We also include a video demonstrating the system.

2021

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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.

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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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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.

2019

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Improving Low-Resource Morphological Learning with Intermediate Forms from Finite State Transducers
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

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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A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68% (unambiguous verbs) and 92.90% (all verbs).

2017

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Creating lexical resources for polysynthetic languages—the case of Arapaho
Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages