A Statistical Model for Automatic Extraction of Korean Transliterated

In this paper, we will describe a Korean transliterated foreign word extraction algorithm. In the proposed method, we reformulate the foreign word extraction problem as a syllable-tagging problem such that each syllable is tagged with a foreign syllable tag or a pure Korean syllable tag. Syllable sequences of Korean strings are modelled by Hidden Markov Model whose state represents a character with binary marking to indicate whether the syllable is part of a transliterated foreign word or not. The proposed method extracts a transliterated foreign word with high recall rate and precision rate. Moreover, our method shows good performance even with small-sized training corpora.
Publisher
World Scientific Publishing
Issue Date
2003-03
Keywords

Computer Science

Citation

A maximum entropy approach to natural language processing, Vol. 16, No. 1, pp. 41–62

DOI
10.1142/S021942790300084X
URI
http://hdl.handle.net/10203/5713
Appears in Collection
CS-Conference Papers(학술회의논문)
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