Structured learning for temporal relation extraction from clinical records

Artuur Leeuwenberg, Marie Francine Moens

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

34 Citations (Scopus)

Abstract

We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR).We employ a structured perceptron, together with integer linear programming constraints for document-level inference during training and prediction to exploit relational properties of temporality, together with global learning of the relations at the document level. Moreover, this study gives insights in the results of integrating constraints for temporal relation extraction when using structured learning and prediction. Our best system outperforms the state-of-the art on both the CONTAINS TLINK task, and the DCTR task.

Original languageEnglish
Title of host publicationLong Papers - Continued
PublisherAssociation for Computational Linguistics (ACL)
Pages1150-1158
Number of pages9
ISBN (Electronic)9781510838604
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017

Publication series

Name15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
Volume2

Conference

Conference15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
Country/TerritorySpain
CityValencia
Period3/04/177/04/17

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