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A Structured Learning Approach to Temporal Relation Extraction

arXiv 1906.04943 ↗First posted 12 Jun 2019cs.CL cs.LGcited by 25

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Abstract (arXiv)

Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jun 2019.

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Citing papers per year: 2021: 7; 2022: 7; 2023: 2; 2024: 4; 2025: 2; 2026: 3.

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  1. 2022MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extractioncited by 66
  2. 2023Zero-shot Temporal Relation Extraction with ChatGPTcited by 53
  3. 2023Test of Time: Instilling Video-Language Models with a Sense of Timecited by 51
  4. 2021LOME: Large Ontology Multilingual Extractioncited by 23
  5. 2021What is Event Knowledge Graph: A Surveycited by 19
  6. 2022Generic Temporal Reasoning with Differential Analysis and Explanationcited by 18
  7. 2024UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorizationcited by 17
  8. 2021Extracting Temporal Event Relation with Syntax-guided Graph Transformercited by 16
  9. 2022ROCK: Causal Inference Principles for Reasoning about Commonsense Causalitycited by 16
  10. 2021Exploring Transitivity in Neural NLI Models through Veridicalitycited by 15
  11. 2022Go Back in Time: Generating Flashbacks in Stories with Event Temporal Promptscited by 15
  12. 2022Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extractioncited by 15
13 more
  1. 2021A Survey on Deep Learning Event Extraction: Approaches and Applicationscited by 12
  2. 2024From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verificationcited by 8
  3. 2024Analysing zero-shot temporal relation extraction on clinical notes using temporal consistencycited by 7
  4. 2024Are LLMs Good Annotators for Discourse-level Event Relation Extraction?cited by 7
  5. 2022Distilling Multi-Scale Knowledge for Event Temporal Relation Extractioncited by 6
  6. 2025Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approachcited by 4
  7. 2025A systematic review of relation extraction task since the emergence of Transformerscited by 1
  8. 2021STAGE: Tool for Automated Extraction of Semantic Time Cues to Enrich Neural Temporal Ordering Modelscited by 0
  9. 2021Context-Dependent Semantic Parsing for Temporal Relation Extractioncited by 0
  10. 2022Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Modelcited by 0
  11. 2026Learning Page Order in Shuffled WOO Releasescited by 0
  12. 2026Looking for the Bottleneck in Fine-grained Temporal Relation Classificationcited by 0
  13. 2026Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narrativescited by 0