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2006

Discriminative Classifiers for Deterministic Dependency Parsing

8 years 7 months ago
Discriminative Classifiers for Deterministic Dependency Parsing
Deterministic parsing guided by treebankinduced classifiers has emerged as a simple and efficient alternative to more complex models for data-driven parsing. We present a systematic comparison of memory-based learning (MBL) and support vector machines (SVM) for inducing classifiers for deterministic dependency parsing, using data from Chinese, English and Swedish, together with a variety of different feature models. The comparison shows that SVM gives higher accuracy for richly articulated feature models across all languages, albeit with considerably longer training times. The results also confirm that classifier-based deterministic parsing can achieve parsing accuracy very close to the best results reported for more complex parsing models.
Johan Hall, Joakim Nivre, Jens Nilsson
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Johan Hall, Joakim Nivre, Jens Nilsson
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