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Evaluating LSTM models for grammatical function labelling

  • To improve grammatical function labelling for German, we augment the labelling component of a neural dependency parser with a decision history. We present different ways to encode the history, using different LSTM architectures, and show that our models yield significant improvements, resulting in a LAS for German that is close to the best result from the SPMRL 2014 shared task (without the reranker).

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Metadaten
Author:Bich-Ngoc Do, Ines Rehbein
URN:urn:nbn:de:bsz:mh39-80010
URL:http://aclweb.org/anthology/W17-6318
ISBN:978-1-945626-73-9
Parent Title (English):Proceedings of the 15th International Conference on Parsing Technologies, September 20–22, 2017 Pisa, Italy (IWPT 2017)
Publisher:The Association for Computational Linguistics
Place of publication:Stroudsburg PA, USA
Document Type:Conference Proceeding
Language:English
Year of first Publication:2017
Date of Publication (online):2018/09/27
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
GND Keyword:Automatische Sprachverarbeitung; Deutsch; Parser; Syntaktische Analyse
First Page:128
Last Page:133
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Leibniz-Classification:Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Open Access?:Ja
Licence (German):License LogoCreative Commons - Namensnennung 4.0 International