Duluth at SemEval-2021 Task 11: Applying DeBERTa to Contributing Sentence Selection and Dependency Parsing for Entity Extraction

Anna Martin, Ted Pedersen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This paper describes the Duluth system that participated in SemEval-2021 Task 11, NLP Contribution Graph. It details the extraction of contribution sentences and scientific entities and their relations from scholarly articles in the domain of Natural Language Processing. Our solution uses deBERTa for multi-class sentence classification to extract the contributing sentences and their type, and dependency parsing to extract phrases from each sentence and format into subject-predicate-object triples. Our system ranked fifth of seven for Phase 1: end-to-end pipeline, sixth of eight for Phase 2 Part 1: phrases and triples, and fifth of eight for Phase 2 Part 2: triples extraction.

Original languageEnglish (US)
Title of host publicationSemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop
EditorsAlexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
PublisherAssociation for Computational Linguistics (ACL)
Pages490-501
Number of pages12
ISBN (Electronic)9781954085701
StatePublished - 2021
Event15th International Workshop on Semantic Evaluation, SemEval 2021 - Virtual, Bangkok, Thailand
Duration: Aug 5 2021Aug 6 2021

Publication series

NameSemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop

Conference

Conference15th International Workshop on Semantic Evaluation, SemEval 2021
Country/TerritoryThailand
CityVirtual, Bangkok
Period8/5/218/6/21

Bibliographical note

Publisher Copyright:
© 2021 Association for Computational Linguistics.

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