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GeCoTagger: annotation of German verb complements with conditional random fields

  • Complement phrases are essential for constructing well-formed sentences in German. Identifying verb complements and categorizing complement classes is challenging even for linguists who are specialized in the field of verb valency. Against this background, we introduce an ML-based algorithm which is able to identify and classify complement phrases of any German verb in any written sentence context. We use a large training set consisting of example sentences from a valency dictionary, enriched with POS tagging, and the ML-based technique of Conditional Random Fields (CRF) to generate the classification models.

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Metadaten
Author:Monica Fürbacher, Roman Schneider
URN:urn:nbn:de:bsz:mh39-74887
URL:http://www.lrec-conf.org/proceedings/lrec2018/summaries/73.html
ISBN:979-10-95546-00-9
Parent Title (English):Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018), 7-12 May 2018, Miyazaki, Japan
Publisher:European language resources association (ELRA)
Place of publication:Paris, France
Editor:Nicoletta Calzolari, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Koiti Hasida, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis, Takenobu Tokunaga
Document Type:Part of a Book
Language:English
Year of first Publication:2018
Date of Publication (online):2018/05/24
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
Tag:grammar and syntax; machine learning methods; verb valency
GND Keyword:Deutsch; Ergänzung <Linguistik>; Grammatik; Valenz <Linguistik>; Verb
First Page:2169
Last Page:2174
Dewey Decimal Classification:400 Sprache / 430 Deutsch
BDSL-Classification:Grammatik
Leibniz-Classification:Sprache, Linguistik
Linguistics-Classification:Grammatikforschung
Open Access?:Ja
Licence (English):License LogoCreative Commons - Attribution-NonCommercial 4.0 International