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Improved head-driven statistical models for natural language parsing

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Abstract

Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, “valency” is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling (SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.

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Authors and Affiliations

  1. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Li-chi Yuan  (袁里驰)

  2. Jiangxi Key Laboratory of Data and Knowledge Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Li-chi Yuan  (袁里驰)

Authors
  1. Li-chi Yuan  (袁里驰)
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Correspondence to Li-chi Yuan  (袁里驰).

Additional information

Foundation item: Project(61262035) supported by the National Natural Science Foundation of China; Projects(GJJ12271, GJJ12742) supported by the Science and Technology Foundation of Education Department of Jiangxi Province, China; Project(20122BAB201033) supported by the Natural Science Foundation of Jiangxi Province, China

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Yuan, Lc. Improved head-driven statistical models for natural language parsing. J. Cent. South Univ. 20, 2747–2752 (2013). https://doi.org/10.1007/s11771-013-1793-3

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  • DOI: https://doi.org/10.1007/s11771-013-1793-3

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