Robust and efficient Chinese word dependency analysis with linear kernel support vector machines

Yu Chieh Wu, Jie Chi Yang, Yue Shi Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Data-driven learning based on shift reduce parsing algorithms has emerged dependency parsing and shown excellent performance to many Tree-banks. In this paper, we investigate the extension of those methods while considerably improved the runtime and training time efficiency via L2-SVMs. We also present several properties and constraints to enhance the parser completeness in runtime. We further integrate root-level and bottom-level syntactic information by using sequential taggers. The experimental results show the positive effect of the root-level and bottom-level features that improve our parser from 81.17% to 81.41% and 81.16% to 81.57% labeled attachment scores with modified Yamada's and Nivre's method, respectively on the Chinese Treebank. In comparison to well-known parsers, such as Malt-Parser (80.74%) and MSTParser (78.08%), our methods produce not only better accuracy, but also drastically reduced testing time in 0.07 and 0.11, respectively.

Original languageEnglish
Title of host publicationColing 2008 - 22nd International Conference on Computational Linguistics, Proceedings of the Conference
Pages135-138
Number of pages4
StatePublished - 2008
Event22nd International Conference on Computational Linguistics, Coling 2008 - Manchester, United Kingdom
Duration: 18 Aug 200822 Aug 2008

Publication series

NameColing 2008 - 22nd International Conference on Computational Linguistics, Proceedings of the Conference
Volume1

Conference

Conference22nd International Conference on Computational Linguistics, Coling 2008
Country/TerritoryUnited Kingdom
CityManchester
Period18/08/0822/08/08

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