A Token Classification Approach to Dependency Parsing

Milidiu&#x;, R.L.,Carlos Eduardo Meger Crestana,Cícero Nogueira dos Santos

Information and Human Language Technology（2009）

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摘要

The Dependency-based syntactic parsing task consists in identifying a head word for each word in an input sentence. Hence, its output is a rooted tree where the nodes are the words in the sentence. State-of-the-art dependency parsing systems use transition-based or graph-based models. We present a token classification approach to dependency parsing, where any classification algorithm can be used. To evaluate its effectiveness, we apply the Entropy Guided Transformation Learning algorithm to the CoNLL 2006 corpus, using the Unlabelled Attachment Score as the accuracy metric. Our results show that the generated models are close to the average CoNLL system performance. Additionally, these findings also indicate that the token classification approach is a promising one.

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关键词

dependency parsing,input sentence,unlabelled attachment score,dependency-based syntactic,state-of-the-art dependency,average conll system performance,head word,classification algorithm,entropy guidedtransformation learning algorithm,token classification approach,entropy,parsing,dependency,accuracy,system performance,natural language processing,transformative learning,grammars,predictive models,measurement,graph theory

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