Abstract
Most current dependency parsers presuppose
that input words have been morphologically
disambiguated using a part-of-speech tagger
before parsing begins. We present a transitionbased system for joint part-of-speech tagging
and labeled dependency parsing with nonprojective trees. Experimental evaluation on
Chinese, Czech, English and German shows
consistent improvements in both tagging and
parsing accuracy when compared to a pipeline
system, which lead to improved state-of-theart results for all languages.
that input words have been morphologically
disambiguated using a part-of-speech tagger
before parsing begins. We present a transitionbased system for joint part-of-speech tagging
and labeled dependency parsing with nonprojective trees. Experimental evaluation on
Chinese, Czech, English and German shows
consistent improvements in both tagging and
parsing accuracy when compared to a pipeline
system, which lead to improved state-of-theart results for all languages.
Original language | English |
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Title of host publication | Association for Computational Linguistics |
Subtitle of host publication | Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning |
Pages | 1455-1465 |
Number of pages | 10 |
Publication status | Published - 2012 |