Automatic chinese text classification using character-based and word-based approach

Xi Luo; Wataru Ohyama; Tetsushi Wakabayashi; Fumitaka Kimura

doi:10.1109/ICDAR.2013.73

Automatic chinese text classification using character-based and word-based approach

Xi Luo, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

In this paper, we study on Chinese text classification using character-based approach (N-gram) and word-based approach and propose the use of uni-gram, bi-gram and word features of length greater than or equal to three. A weight coefficient which can be used to give higher weights to word features is also introduced. We further investigate a serial approach based on feature transformation and dimension reduction techniques to improve the performance. Experimental results show that our proposed approach is efficient and effective for improving the performance of Chinese text classification.

Original language	English
Article number	6628638
Pages (from-to)	329-333
Number of pages	5
Journal	Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
DOIs	https://doi.org/10.1109/ICDAR.2013.73
Publication status	Published - 2013
Externally published	Yes
Event	12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, United States Duration: Aug 25 2013 → Aug 28 2013

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition

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10.1109/ICDAR.2013.73

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title = "Automatic chinese text classification using character-based and word-based approach",

abstract = "In this paper, we study on Chinese text classification using character-based approach (N-gram) and word-based approach and propose the use of uni-gram, bi-gram and word features of length greater than or equal to three. A weight coefficient which can be used to give higher weights to word features is also introduced. We further investigate a serial approach based on feature transformation and dimension reduction techniques to improve the performance. Experimental results show that our proposed approach is efficient and effective for improving the performance of Chinese text classification.",

author = "Xi Luo and Wataru Ohyama and Tetsushi Wakabayashi and Fumitaka Kimura",

year = "2013",

doi = "10.1109/ICDAR.2013.73",

language = "English",

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journal = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",

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note = "12th International Conference on Document Analysis and Recognition, ICDAR 2013 ; Conference date: 25-08-2013 Through 28-08-2013",

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T1 - Automatic chinese text classification using character-based and word-based approach

AU - Luo, Xi

AU - Ohyama, Wataru

AU - Wakabayashi, Tetsushi

AU - Kimura, Fumitaka

PY - 2013

Y1 - 2013

N2 - In this paper, we study on Chinese text classification using character-based approach (N-gram) and word-based approach and propose the use of uni-gram, bi-gram and word features of length greater than or equal to three. A weight coefficient which can be used to give higher weights to word features is also introduced. We further investigate a serial approach based on feature transformation and dimension reduction techniques to improve the performance. Experimental results show that our proposed approach is efficient and effective for improving the performance of Chinese text classification.

AB - In this paper, we study on Chinese text classification using character-based approach (N-gram) and word-based approach and propose the use of uni-gram, bi-gram and word features of length greater than or equal to three. A weight coefficient which can be used to give higher weights to word features is also introduced. We further investigate a serial approach based on feature transformation and dimension reduction techniques to improve the performance. Experimental results show that our proposed approach is efficient and effective for improving the performance of Chinese text classification.

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U2 - 10.1109/ICDAR.2013.73

DO - 10.1109/ICDAR.2013.73

M3 - Conference article

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JO - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

JF - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

M1 - 6628638

T2 - 12th International Conference on Document Analysis and Recognition, ICDAR 2013

Y2 - 25 August 2013 through 28 August 2013

ER -

Automatic chinese text classification using character-based and word-based approach

Abstract

All Science Journal Classification (ASJC) codes

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