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Learning Verb Classes in an Incremental Model

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

Abstract

The ability of children to generalize over the linguistic input they receive is key to acquiring productive knowledge of verbs. Such generalizations help children extend their learned knowledge of constructions to a novel verb, and use it appropriately in syntactic patterns previously unobserved for that verb-a key factor in language productivity. Computational models can help shed light on the gradual development of more abstract knowledge during verb acquisition. We present an incremental Bayesian model that simultaneously and incrementally learns argument structure constructions and verb classes given naturalistic language input. We show how the distributional properties in the input language influence the formation of generalizations over the constructions and classes.

Original languageEnglish
Title of host publication5th Workshop on Cognitive Modeling and Computational Linguistics, CMCL 2014 at the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings
EditorsVera Demberg, Tim O�Donnell
PublisherAssociation for Computational Linguistics (ACL)
Pages37-45
Number of pages9
ISBN (Electronic)9781941643051
StatePublished - 2014
Event5th Workshop on Cognitive Modeling and Computational Linguistics, CMCL 2014 at the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, United States
Duration: 26 Jun 2014 → …

Publication series

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

Conference

Conference5th Workshop on Cognitive Modeling and Computational Linguistics, CMCL 2014 at the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
Country/TerritoryUnited States
CityBaltimore
Period26/06/14 → …

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