mlsanityData to 7 Oct 2026

EventGraph: Event Extraction as Semantic Graph Parsing

arXiv 2210.08646 ↗First posted 16 Oct 2022cs.CLcited by 4

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Abstract (arXiv)

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. Additionally, we create two new datasets from ACE2005 where we keep the entire text spans for event arguments, instead of just the head word(s). Our code and models are released as open-source.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 16 Oct 2022.

Builds on

19 of 43 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  4. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  5. 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
  6. 2019Entity, Relation, and Event Extraction with Contextualized Span Representationscited by 261
  7. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  8. 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
  9. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 154
  10. 2020Named Entity Recognition as Dependency Parsingcited by 150
  11. 2021Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extractioncited by 129
  12. 2018Jointly Multiple Events Extraction via Attention-based Graph Information Aggregationcited by 73
7 more
  1. 2016Joint Extraction of Events and Entities within a Document Contextcited by 48
  2. 2018Simpler but More Accurate Semantic Dependency Parsingcited by 46
  3. 2022Dynamic Prefix-Tuning for Generative Template-based Event Extractioncited by 35
  4. 2021Structured Sentiment Analysis as Dependency Graph Parsingcited by 28
  5. 2017A Transition-Based Directed Acyclic Graph Parser for UCCAcited by 13
  6. 2020'UFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERINcited by 12
  7. 2022Direct parsing to sentiment graphscited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 3.

4 papers.

  1. 2023MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotationcited by 6
  2. 2022Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Reportcited by 4
  3. 2023A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extractioncited by 2
  4. 2023JSEEGraph: Joint Structured Event Extraction as Graph Parsingcited by 2