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Title
Abstract
Introduction
Previous Work on Relation Extraction in Ir and
Background and Related Work
Dataset
Data Collection
Annotation Procedure
Analysis
Results and Analysis
Case Study
Conclusion
References
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Computer Science
Artificial Intelligence

EDeR: A Dataset for Exploring Dependency Relations Between Events

Profile image of leyang cuileyang cui

2023, arXiv (Cornell University)

https://doi.org/10.48550/ARXIV.2304.01612
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15 pages

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Abstract

Relation extraction is a central task in natural language processing (NLP) and information retrieval (IR) research. We argue that an important type of relation not explored in NLP or IR research to date is that of an event being an argument-required or optionalof another event. We introduce the humanannotated Event Dependency Relation dataset (EDeR) which provides this dependency relation. The annotation is done on a sample of documents from the OntoNotes dataset, which has the added benefit that it integrates with existing, orthogonal, annotations of this dataset. We investigate baseline approaches for predicting the event dependency relation, the best of which achieves an accuracy of 82.61% for binary argument/non-argument classification. We show that recognizing this relation leads to more accurate event extraction (semantic role labelling) and can improve downstream tasks that depend on this, such as co-reference resolution. Furthermore, we demonstrate that predicting the three-way classification into the required argument, optional argument or nonargument is a more challenging task.

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Syntactic simplification and semantic enrichment—trimming dependency graphs for event extraction
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In our approach to event extraction, dependency graphs constitute the fundamental data structure for knowledge capture. Two types of trimming operations pave the way to more effective relation extraction. First, we simplify the syntactic representation structures resulting from parsing by pruning informationally irrelevant lexical material from dependency graphs. Second, we enrich informationally relevant lexical material in the simplified dependency graphs with additional semantic meta data at several layers of conceptual granularity. These two aggregation operations on linguistic representation structures are intended to avoid overfitting of machine learning-based classifiers which we use for event extraction (besides manually curated dictionaries). Given this methodological framework, the corresponding JREX system developed by the JULIELab Team from Friedrich-Schiller-Universität Jena (Germany) scored on 2nd rank among 24 competing teams for Task 1 in the "BioNLP'09 Shared Task on Event Extraction," with 45.8% recall, 47.5% precision and 46.7% F1-score on all 3,182 events. In more recent experiments, based on slight modifications of JREX and using the same data sets, we were able to achieve 45.9% recall, 57.7% precision, and 51.1% F1-score.

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We describe the SemEval-2010 shared task on "Linking Events and Their Participants in Discourse". This task is an extension to the classical semantic role labeling task. While semantic role labeling is traditionally viewed as a sentence-internal task, local semantic argument structures clearly interact with each other in a larger context, e.g., by sharing references to specific discourse entities or events. In the shared task we looked at one particular aspect of cross-sentence links between argument structures, namely linking locally uninstantiated roles to their co-referents in the wider discourse context (if such co-referents exist). This task is potentially beneficial for a number of NLP applications, such as information extraction, question answering or text summarization.

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Evaluating the impact of alternative dependency graph encodings on solving event extraction tasks
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Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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