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).
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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.
