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Brown clustering for unlexicalized parsing

  • Brown clustering has been used to help increase parsing performance for morphologically rich languages. However, much of the work has focused on using clustering techniques to replace terminal nodes or as a feature for parsing. Instead, we choose to examine how effectively Brown clustering is for unlexicalized parsing by creating data-driven POS tagsets which are then used with the Berkeley parser. We investigate cluster sizes as well as on what information (e.g. words vs. lemmas) clustering will yield the best parser performance. Our results approach the current state of the art results for the German T¨uBa-D/Z treebank when using parser internal tagging.

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Metadaten
Author:Daniel Dakota
URN:urn:nbn:de:bsz:mh39-61818
URL:https://www.linguistics.rub.de/bla/
ISSN:2190-0949
Parent Title (English):Proceedings of the 13th Conference on Natural Language Processing (KONVENS) Bochum, Germany September 19–21, 2016
Series (Serial Number):Bochumer Linguistische Arbeitsberichte (16)
Publisher:Ruhr-Universität Bochum
Place of publication:Bochum
Translator:Stefanie Dipper, Friedrich Neubarth, Heike Zinsmeister
Document Type:Part of a Book
Language:English
Year of first Publication:2016
Date of Publication (online):2017/05/23
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
Tag:Brown clustering
GND Keyword:Automatische Sprachanalyse;; Cluster <Datenanalyse>; Deutsch
First Page:68
Last Page:77
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Linguistics-Classification:Korpuslinguistik
Open Access?:Ja
Licence (German):Es gilt das UrhG