Machine reading of historical events

Or Honovich, Lucas Torroba Hennigen*, Omri Abend, Shay B. Cohen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities. Within this broad framework, we address the task of machine reading the time of historical events, compile datasets for the task, and develop a model for tackling it. Given a brief textual description of an event, we show that good performance can be achieved by extracting relevant sentences from Wikipedia, and applying a combination of task-specific and general-purpose feature embeddings for the classification. Furthermore, we establish a link between the historical event ordering task and the event focus time task from the information retrieval literature, showing they also provide a challenging test case for machine reading algorithms.

Original languageAmerican English
Title of host publicationACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
EditorsDan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
PublisherAssociation for Computational Linguistics (ACL)
Number of pages12
ISBN (Electronic)9781952148255
StatePublished - 2020
Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: 5 Jul 202010 Jul 2020

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X


Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/TerritoryUnited States
CityVirtual, Online

Bibliographical note

Funding Information:
We thank the anonymous reviewers for helpful feedback. We would also like to thank Maximin Coavoux, Simone Teufel, and Ryan Cotterell for their help and comments. We gratefully acknowledge the support of Bloomberg (Cohen). This work was partially supported by the Israel Science Foundation (grant No. 929/17)

Publisher Copyright:
© 2020 Association for Computational Linguistics


  • Machine reading
  • Natural language processing
  • NLP
  • information retrieval
  • algorithms


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