Paths to Relation Extraction through Semantic Structures

Jonathan Yellin, Omri Abend

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


Syntactic and semantic structure directly reflect relations expressed by the text at hand and are thus very useful for the relation extraction (RE) task. Their symbolic nature allows increased interpretability for end-users and developers, which is particularly appealing in RE. Although they have been somewhat overshadowed recently by the use of end-to-end neural network models and contextualized word embeddings, we show that they may be leveraged as input for neural networks to positive effect. We present two methods for integrating broad-coverage semantic structure (specifically, UCCA) into supervised neural RE models, demonstrating benefits over the use of exclusively syntactic integrations. The first method involves reduction of UCCA into a bilexical structure, while the second leverages a novel technique for encoding semantic DAG structures. Our approach is general and can be used for integrating a wide range of graph-based semantic structures.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationACL-IJCNLP 2021
EditorsChengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
PublisherAssociation for Computational Linguistics
Number of pages13
StatePublished - Aug 2021
EventFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021 - Virtual, Online
Duration: 1 Aug 20216 Aug 2021

Publication series

NameFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021


ConferenceFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021 Association for Computational Linguistics


  • Syntactic structure
  • semantic structure
  • relation extraction
  • RE
  • neural networks
  • UCCA


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