Automatic prediction of aspectual class of verbs in context

  • This paper describes a new approach to predicting the aspectual class of verbs in context, i.e., whether a verb is used in a stative or dynamic sense. We identify two challenging cases of this problem: when the verb is unseen in training data, and when the verb is ambiguous for aspectual class. A semi-supervised approach using linguistically-motivated features and a novel set of distributional features based on representative verb types allows us to predict classes accurately, even for unseen verbs. Many frequent verbs can be either stative or dynamic in different contexts, which has not been modeled by previous work; we use contextual features to resolve this ambiguity. In addition, we introduce two new datasets of clauses marked for aspectual class.

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Metadaten
Author:Annemarie FriedrichORCiDGND, Alexis Palmer
URN:urn:nbn:de:bvb:384-opus4-1057108
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/105710
ISBN:978-1-937284-73-2OPAC
Parent Title (English):Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), June 22-27, 2014, Baltimore, Maryland, USA
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA
Editor:Kristina Toutanova, Hua Wu
Type:Conference Proceeding
Language:English
Year of first Publication:2014
Publishing Institution:Universität Augsburg
Release Date:2023/07/10
First Page:517
Last Page:523
DOI:https://doi.org/10.3115/v1/p14-2085
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Sprachverstehen mit der Anwendung Digital Humanities
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Sonstige Open-Access-Lizenz