A fast and accurate Vietnamese word segmenter

Dat Quoc Nguyen, Dai Quoc Nguyen, Thanh Vu, Mark Dras, Mark Johnson

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

Abstract

We propose a novel approach to Vietnamese word segmentation. Our approach is based on the Single Classification Ripple Down Rules methodology (Compton and Jansen, 1990), where rules are stored in an exception structure and new rules are only added to correct segmentation errors given by existing rules. Experimental results on the benchmark Vietnamese treebank show that our approach outperforms previous state-of-the-art approaches JVnSegmenter, vnTokenizer, DongDu and UETsegmenter in terms of both accuracy and performance speed. Our code is open-source and available at: https://github.com/datquocnguyen/RDRsegmenter.

LanguageEnglish
Title of host publicationLREC 2018 - 11th International Conference on Language Resources and Evaluation
EditorsNicoletta Calzolari, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Koiti Hasida, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis, Takenobu Tokunaga
PublisherEuropean Language Resources Association (ELRA)
Pages2582-2587
Number of pages6
ISBN (Electronic)9791095546009
Publication statusPublished - 1 Jan 2019
Event11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, Japan
Duration: 7 May 201812 May 2018

Conference

Conference11th International Conference on Language Resources and Evaluation, LREC 2018
CountryJapan
CityMiyazaki
Period7/05/1812/05/18

Fingerprint

methodology
performance
segmentation
Segmentation
Treebank
Open Source
Methodology
Word Segmentation
Benchmark

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Single classification ripple down rules
  • Vietnamese
  • Word segmentation

Cite this

Nguyen, D. Q., Nguyen, D. Q., Vu, T., Dras, M., & Johnson, M. (2019). A fast and accurate Vietnamese word segmenter. In N. Calzolari, K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, ... T. Tokunaga (Eds.), LREC 2018 - 11th International Conference on Language Resources and Evaluation (pp. 2582-2587). European Language Resources Association (ELRA).
Nguyen, Dat Quoc ; Nguyen, Dai Quoc ; Vu, Thanh ; Dras, Mark ; Johnson, Mark. / A fast and accurate Vietnamese word segmenter. LREC 2018 - 11th International Conference on Language Resources and Evaluation. editor / Nicoletta Calzolari ; Khalid Choukri ; Christopher Cieri ; Thierry Declerck ; Sara Goggi ; Koiti Hasida ; Hitoshi Isahara ; Bente Maegaard ; Joseph Mariani ; Helene Mazo ; Asuncion Moreno ; Jan Odijk ; Stelios Piperidis ; Takenobu Tokunaga. European Language Resources Association (ELRA), 2019. pp. 2582-2587
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abstract = "We propose a novel approach to Vietnamese word segmentation. Our approach is based on the Single Classification Ripple Down Rules methodology (Compton and Jansen, 1990), where rules are stored in an exception structure and new rules are only added to correct segmentation errors given by existing rules. Experimental results on the benchmark Vietnamese treebank show that our approach outperforms previous state-of-the-art approaches JVnSegmenter, vnTokenizer, DongDu and UETsegmenter in terms of both accuracy and performance speed. Our code is open-source and available at: https://github.com/datquocnguyen/RDRsegmenter.",
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year = "2019",
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Nguyen, DQ, Nguyen, DQ, Vu, T, Dras, M & Johnson, M 2019, A fast and accurate Vietnamese word segmenter. in N Calzolari, K Choukri, C Cieri, T Declerck, S Goggi, K Hasida, H Isahara, B Maegaard, J Mariani, H Mazo, A Moreno, J Odijk, S Piperidis & T Tokunaga (eds), LREC 2018 - 11th International Conference on Language Resources and Evaluation. European Language Resources Association (ELRA), pp. 2582-2587, 11th International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, 7/05/18.

A fast and accurate Vietnamese word segmenter. / Nguyen, Dat Quoc; Nguyen, Dai Quoc; Vu, Thanh; Dras, Mark; Johnson, Mark.

LREC 2018 - 11th International Conference on Language Resources and Evaluation. ed. / Nicoletta Calzolari; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Koiti Hasida; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Helene Mazo; Asuncion Moreno; Jan Odijk; Stelios Piperidis; Takenobu Tokunaga. European Language Resources Association (ELRA), 2019. p. 2582-2587.

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

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N2 - We propose a novel approach to Vietnamese word segmentation. Our approach is based on the Single Classification Ripple Down Rules methodology (Compton and Jansen, 1990), where rules are stored in an exception structure and new rules are only added to correct segmentation errors given by existing rules. Experimental results on the benchmark Vietnamese treebank show that our approach outperforms previous state-of-the-art approaches JVnSegmenter, vnTokenizer, DongDu and UETsegmenter in terms of both accuracy and performance speed. Our code is open-source and available at: https://github.com/datquocnguyen/RDRsegmenter.

AB - We propose a novel approach to Vietnamese word segmentation. Our approach is based on the Single Classification Ripple Down Rules methodology (Compton and Jansen, 1990), where rules are stored in an exception structure and new rules are only added to correct segmentation errors given by existing rules. Experimental results on the benchmark Vietnamese treebank show that our approach outperforms previous state-of-the-art approaches JVnSegmenter, vnTokenizer, DongDu and UETsegmenter in terms of both accuracy and performance speed. Our code is open-source and available at: https://github.com/datquocnguyen/RDRsegmenter.

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Nguyen DQ, Nguyen DQ, Vu T, Dras M, Johnson M. A fast and accurate Vietnamese word segmenter. In Calzolari N, Choukri K, Cieri C, Declerck T, Goggi S, Hasida K, Isahara H, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, Piperidis S, Tokunaga T, editors, LREC 2018 - 11th International Conference on Language Resources and Evaluation. European Language Resources Association (ELRA). 2019. p. 2582-2587