A Learning Approach to Shallow Parsing

ArXiv: cs/0008022
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Abstract

A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably with the best published results are presented. In doing that, we compare two ways of modeling the problem of learning to recognize patterns and suggest that shallow parsing patterns are better learned using open/close predictors than using inside/outside predictors.

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APA

Muñoz, M., Punyakanok, V., Roth, D., & Zimak, D. (1999). A Learning Approach to Shallow Parsing. In Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, EMNLP 1999 (pp. 168–178). Association for Computational Linguistics (ACL).

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