Starting from scratch in Semantic Role Labeling

Michael Connor, Yael Gertner, Cynthia L Fisher, Dan Roth

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

Abstract

A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate arguments, and assign semantic roles to those constituents. Each step depends on prior lexical and syntactic knowledge. Where do children learning their first languages begin in solving this problem? In this paper we focus on the parsing and argumentidentification steps that precede Semantic Role Labeling (SRL) training. We combine a simplified SRL with an unsupervised HMM part of speech tagger, and experiment with psycholinguisticallymotivated ways to label clusters resulting from the HMM so that they can be used to parse input for the SRL system. The results show that proposed shallow representations of sentence structure are robust to reductions in parsing accuracy, and that the contribution of alternative representations of sentence structure to successful semantic role labeling varies with the integrity of the parsing and argumentidentification stages.

Original languageEnglish (US)
Title of host publicationACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Pages989-998
Number of pages10
StatePublished - Dec 1 2010
Event48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Uppsala, Sweden
Duration: Jul 11 2010Jul 16 2010

Publication series

NameACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Other

Other48th Annual Meeting of the Association for Computational Linguistics, ACL 2010
CountrySweden
CityUppsala
Period7/11/107/16/10

Fingerprint

semantics
listener
integrity
Labeling
Semantic Roles
comprehension
candidacy
experiment
language
learning
Constituent
Parsing
Hidden Markov Model

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Connor, M., Gertner, Y., Fisher, C. L., & Roth, D. (2010). Starting from scratch in Semantic Role Labeling. In ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 989-998). (ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference).

Starting from scratch in Semantic Role Labeling. / Connor, Michael; Gertner, Yael; Fisher, Cynthia L; Roth, Dan.

ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. 2010. p. 989-998 (ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference).

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

Connor, M, Gertner, Y, Fisher, CL & Roth, D 2010, Starting from scratch in Semantic Role Labeling. in ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, pp. 989-998, 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010, Uppsala, Sweden, 7/11/10.
Connor M, Gertner Y, Fisher CL, Roth D. Starting from scratch in Semantic Role Labeling. In ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. 2010. p. 989-998. (ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference).
Connor, Michael ; Gertner, Yael ; Fisher, Cynthia L ; Roth, Dan. / Starting from scratch in Semantic Role Labeling. ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. 2010. pp. 989-998 (ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference).
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