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General Information
    • ISSN: 2382-6185
    • Abbreviated Title: Int. J. Knowl. Eng.
    • Frequency: Semiyearly
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2015 Vol.1(1): 49-53 ISSN: 2382-6185
DOI: 10.7763/IJKE.2015.V1.8

Joint Model of Korean Part-of-Speech Tagging and Dependency Parsing with Partial Tagged Corpus

Youngmin Park and Jungyun Seo
Abstract—Most recent studies on part-of-speech (POS) tagging and dependency parsing employ a pipelined model design. However, pipelined structures may decrease performance on account of error propagations. Furthermore, syntactic information is required to improve POS tagging performance. In this paper, we propose a joint model of POS tagging and dependency parsing for the Korean language. Our joint model analyzes the maximum score dependency tree with POS tagging using the graph-based CKY parsing method. We present an effective application method for an additional POS tagged corpus and evaluate the method for POS tagging and dependency parsing. The results show that our model improves accuracy by approximately 2.3% and 1.7% more than a pipelined structure using a hidden Markov model and graph-based dependency parsing, respectively.

Index Terms—Part-of-speech tagging, dependency parsing, natural language processing, joint model.

The authors are with the Department of Computer Science and Engineering, Sogang University, Korea (e-mail: pymnlp@gmail.com, seojy@sogang.ac.kr).


Cite: Youngmin Park and Jungyun Seo, "Joint Model of Korean Part-of-Speech Tagging and Dependency Parsing with Partial Tagged Corpus," International Journal of Knowledge Engineering vol. 1, no. 1, pp. 49-53, 2015.

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