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Data point selection for genre-aware parsing

  • In the NLP literature, adapting a parser to new text with properties different from the training data is commonly referred to as domain adaptation. In practice, however, the differences between texts from different sources often reflect a mixture of domain and genre properties, and it is by no means clear what impact each of those has on statistical parsing. In this paper, we investigate how differences between articles in a newspaper corpus relate to the concepts of genre and domain and how they influence parsing performance of a transition-based dependency parser. We do this by applying various similarity measures for data point selection and testing their adequacy for creating genre-aware parsing models.

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Metadaten
Author:Ines Rehbein, Felix Bildhauer
URN:urn:nbn:de:bsz:mh39-80007
URL:https://aclweb.org/anthology/W/W17/W17-7614.pdf
ISBN:978-80-88132-04-2
Parent Title (English):Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories, January 23–24, 2018 Prague, Czech Republic (TLT16)
Publisher:The Association for Computational Linguistics
Place of publication:Stroudsburg PA, USA
Editor:Jan Hajič
Document Type:Conference Proceeding
Language:English
Year of first Publication:2017
Date of Publication (online):2018/09/27
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
Tag:dependency parsing; genre and register variation; parser adaptation
GND Keyword:Korpus <Linguistik>; Parsing; Textsorte
First Page:95
Last Page:105
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Leibniz-Classification:Sprache, Linguistik
Linguistics-Classification:Korpuslinguistik
Open Access?:Ja
Licence (English):License LogoCreative Commons - Attribution 4.0 International