EventGraph: Event Extraction as Semantic Graph Parsing
Huiling You, David Samuel, Samia Touileb, and Lilja {\O}vrelid

TL;DR
EventGraph introduces a novel joint graph-based framework for event extraction that simultaneously detects events and their arguments, capturing complex interactions and improving argument extraction performance.
Contribution
The paper presents a unified graph parsing approach for event extraction, enabling joint detection and argument extraction, and introduces new datasets with full argument spans.
Findings
Competitive performance on ACE2005
Significant improvement in argument extraction accuracy
Effective modeling of event argument interactions
Abstract
Event extraction involves the detection and extraction of both the event triggers and corresponding event arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, which encodes events as graphs. We represent event triggers and arguments as nodes in a semantic graph. Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; and 3) capturing the complicated interaction between event arguments and triggers. Experimental results on ACE2005 show that our model is competitive to state-of-the-art systems and has substantially improved the results on argument extraction.…
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Taxonomy
TopicsNatural Language Processing Techniques · Semantic Web and Ontologies · Topic Modeling
