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CogCompTime: A Tool for Understanding Time in Natural Language Text

arXiv 1906.04940 ↗First posted 12 Jun 2019cs.CL cs.IRcited by 23

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Abstract (arXiv)

Automatic extraction of temporal information in text is an important component of natural language understanding. It involves two basic tasks: (1) Understanding time expressions that are mentioned explicitly in text (e.g., February 27, 1998 or tomorrow), and (2) Understanding temporal information that is conveyed implicitly via relations. In this paper, we introduce CogCompTime, a system that has these two important functionalities. It incorporates the most recent progress, achieves state-of-the-art performance, and is publicly available.1 We believe that this demo will be useful for multiple time-aware applications and provide valuable insight for future research in temporal understanding.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2019.

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Citing papers per year: 2021: 8; 2022: 2; 2023: 8; 2024: 4; 2025: 1.

23 papers.

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  1. 2021A Dataset for Answering Time-Sensitive Questionscited by 150
  2. 2023Test of Time: Instilling Video-Language Models with a Sense of Timecited by 51
  3. 2023TempTabQA: Temporal Question Answering for Semi-Structured Tablescited by 27
  4. 2022There is a Time and Place for Reasoning Beyond the Imagecited by 22
  5. 2021What is Event Knowledge Graph: A Surveycited by 19
  6. 2022Generic Temporal Reasoning with Differential Analysis and Explanationcited by 18
  7. 2021EventPlus: A Temporal Event Understanding Pipelinecited by 17
  8. 2021Probing Language Models for Understanding of Temporal Expressionscited by 16
  9. 2021Extracting Event Temporal Relations via Hyperbolic Geometrycited by 13
  10. 2025It's High Time: A Survey of Temporal Question Answeringcited by 12
  11. 2023Event Temporal Relation Extraction with Bayesian Translational Modelcited by 10
  12. 2023On the Evolution of Knowledge Graphs: A Survey and Perspectivecited by 9
11 more
  1. 2024Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generationcited by 8
  2. 2024Enhancing Temporal Understanding in LLMs for Semi-structured Tablescited by 8
  3. 2023TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articlescited by 7
  4. 2021BERT got a Date: Introducing Transformers to Temporal Taggingcited by 4
  5. 2024Unveiling Divergent Inductive Biases of LLMs on Temporal Datacited by 4
  6. 2021Indian Legal NLP Benchmarks : A Surveycited by 3
  7. 2023Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answeringcited by 3
  8. 2024Cascading Large Language Models for Salient Event Graph Generationcited by 3
  9. 2021Automatic Rule Generation for Time Expression Normalizationcited by 2
  10. 2023RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editorcited by 1
  11. 2023A Modular Approach for Multilingual Timex Detection and Normalization using Deep Learning and Grammar-based methodscited by 0