Situation entity types: automatic classification of clause-level aspect

  • This paper describes the first robust approach to automatically labeling clauses with their situation entity type (Smith, 2003), capturing aspectual phenomena at the clause level which are relevant for interpreting both semantics at the clause level and discourse structure. Previous work on this task used a small data set from a limited domain, and relied mainly on words as features, an approach which is impractical in larger settings. We provide a new corpus of texts from 13 genres (40,000 clauses) annotated with situation entity types. We show that our sequence labeling approach using distributional information in the form of Brown clusters, as well as syntactic-semantic features targeted to the task, is robust across genres, reaching accuracies of up to 76%.

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Metadaten
Author:Annemarie FriedrichORCiDGND, Alexis Palmer, Manfred Pinkal
URN:urn:nbn:de:bvb:384-opus4-1056878
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/105687
ISBN:978-1-945626-00-5OPAC
Parent Title (English):Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), August 7-12 2016, Berlin, Germany
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA
Editor:Katrin Erk, Noah A. Smith
Type:Conference Proceeding
Language:English
Year of first Publication:2016
Publishing Institution:Universität Augsburg
Release Date:2023/07/10
First Page:1757
Last Page:1768
DOI:https://doi.org/10.18653/v1/p16-1166
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):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)

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