Improving cross-domain dependency parsing with dependency-derived clusters

Jostein Lien
Department of Informatics, University of Oslo, Norway

Erik Velldal
Department of Informatics, University of Oslo, Norway

Lilja Øvrelid
Department of Informatics, University of Oslo, Norway

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Ingår i: Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Linköping Electronic Conference Proceedings 109:16, s. 117-126

NEALT Proceedings Series 23:16, p. 117-126

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Publicerad: 2015-05-06

ISBN: 978-91-7519-098-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


This paper describes a semi-supervised approach to improving statistical dependency parsing using dependency-based word clusters. After applying a baseline parser to unlabeled text, clusters are induced using K-means with word features based on the dependency structures. The parser is then re-trained using information about the clusters, yielding improved parsing accuracy on a range of different data sets, including WSJ and the English Web Treebank. We report improved results using both in-domain and out-of-domain data, and also include a comparison with using n-gram-based Brown clustering.


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