POS tagging for German : how important is the right context?

Part-of-Speech tagging is generally performed by Markov models, based on bigram or trigram models. While Markov models have a strong concentration on the left context of a word, many languages require the inclusion of ri
Part-of-Speech tagging is generally performed by Markov models, based on bigram or trigram models. While Markov models have a strong concentration on the left context of a word, many languages require the inclusion of right context for correct disambiguation. We show for German that the best results are reached by a combination of left and right context. If only left context is available, then changing the direction of analysis and going from right to left improves the results. In a version of MBT (Daelemans et al., 1996) with default parameter settings, the inclusion of the right context improved POS tagging accuracy from 94.00% to 96.08%, thus corroborating our hypothesis. The version with optimized parameters reaches 96.73%.
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Metadaten
Author:Steliana Ivanova, Sandra Kübler
URN:urn:nbn:de:hebis:30-1110660
Document Type:Article
Language:German
Date of Publication (online):2008/10/21
Year of first Publication:2008
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2008/10/21
Source:http://jones.ling.indiana.edu/~skuebler/papers/postagging.pdf ; (in:) Proceedings of the Sixth International Conference on Language Resources and Evaluation, LREC 2007 - Marrakesh, 2008.
HeBIS PPN:205688187
Dewey Decimal Classification:400 Sprache
Sammlungen:Linguistik
Linguistic-Classification:Linguistik-Klassifikation: Computerlinguistik / Computational linguistics
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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