Role Assignment to Thai Word Using Multilayer Perception Network

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Published: Nov 12, 2018
Keywords:
Part of speech tagging, Thai language processing, POS tagging with neural network, multilayer perceptron network, role feature tagging

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Ponrudee Netisopakul*
Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
Saichon Jaiyen
Department of Mathematics and Computer Science, Faculty of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
Peerasak Intarapaiboon
Research Center for Communications and Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

Abstract

Thai language has some certain characteristics, which lead to a number of interesting research problems in natural language processing. For instance, Thai words in a sentence do not have feature embedded form, i.e., Thai words always have the same form in any sentences. This partially explains the lack of feature information in Thai part of speech (POS). Thai POS tagging only tags word category, such as noun, pronoun, verb, proposition and so on. Category information alone is inadequate for further language processing. This paper proposes the idea of tagging word role in a sentence using multilayer perceptron as a toll. The role assignment information can be used in complimentary to category assignment in Thai language processing.


Keywords:  Part of speech tagging, Thai language processing, POS tagging with neural network, multilayer perceptron network, role feature tagging


Corresponding author: E-mail: [email protected]

Article Details

Issue
Vol. 5 No. 1 (2005)
Section
Original Research Articles

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References

[1] Meknavin, S., Charoenpornsawat, P., and Kijsirikul B., 1997. Feature-based Thai Word Segmentation. In Proceedings of the Natural Language Processing Pacific Rim Symposium (NLPRS’97), Phuket, Thailand.
[2] Charoenpornsawat, P., Kijsirikul, B., Meknavin, S., 1998. Feature-based Thai unknown word boundary identification using Winnow. IEEE Asia-Pacific Conference, Nov 1998. 547-550.
[3] Sornlertlamvanich, V., Charoenporn T., and Isahara, H., 1997. ORCHID: Thai Part-of-Speech Tagged Corpus. In Technical Report Orchid Corpus.
[4] Ma, Q., 2002. Natural Language Processing with Neural Networks. In Proceedings of the Language Engineering Conference (LEC’02), 13-15 Dec. 2002, 45-46.
[5] Ahmed., Raju S.B., Chandrasekhar Pammi V.S., and Prasad M.K., 2002. Application of Multilayer Perceptron Network for Tagging Parts-of-Speech, Proceedings of the Language Engineering Conference (LEC’02), 13-15 Dec. 2002, 57-63.
[6] Murata, M., Ma, Q., and Isahara, H., 2001. Part of Speech Tagging in Thai Language Using Support Vector Machine. The Second Workshop on Natural Language Processing and Neural Networks (NLPNN’2001).
[7] Netisopakul, P., Keawwan, K., 2004. Simple Thai Sentence Segmentation Using M-ATN. Graduate Project Report, Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang.
[8] Varakulsiripunth, R., Junwun, S., and Maneenate, N., 1989. Thai Syntactical Analysis by M-ATN. Papers on Natural Language Processing: Multi-lingual Machine Translation and Related Topics (1987-1994), 192-202.
[9] Haykin, S., 1998. Neural Networks: A Comprehensive Foundation (2ed), Prentice Hall.