Design and prototype of a large-scale and fully sense-tagged corpus

Sue Jin Ker; Chu Ren Huang; Jia Fei Hong; Shi Yin Liu; Hui Ling Jian; I. Li Su; Shu Kai Hsieh

doi:10.1007/978-3-540-78159-2_18

Design and prototype of a large-scale and fully sense-tagged corpus

Sue Jin Ker^*, Chu Ren Huang, Jia Fei Hong, Shi Yin Liu, Hui Ling Jian, I. Li Su, Shu Kai Hsieh

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Sense tagged corpus plays a very crucial role to Natural Language Processing, especially on the research of word sense disambiguation and natural language understanding. Having a large-scale Chinese sense tagged corpus seems to be very essential, but in fact, such large-scale corpus is the critical deficiency at the current stage. This paper is aimed to design a large-scale Chinese full text sense tagged Corpus, which contains over 110,000 words. The Academia Sinica Balanced Corpus of Modern Chinese (also named Sinica Corpus) is treated as the tagging object, and there are 56 full texts extracted from this corpus. By using the N-gram statistics and the information of collocation, the preparation work for automatic sense tagging is planned by combining the techniques and methods of machine learning and the probability model. In order to achieve a highly precise result, the result of automatic sense tagging needs the touch of manual revising.

Original language	English
Title of host publication	Large-Scale Knowledge Resources
Subtitle of host publication	Construction and Application - Third International Conference on Large-Scale Knowledge Resources, LKR 2008, Proceedings
Pages	186-193
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-540-78159-2_18
Publication status	Published - 2008
Externally published	Yes
Event	3rd International Conference on Large-Scale Knowledge Resources, LKR 2008 - Tokyo, Japan Duration: 2008 Mar 3 → 2008 Mar 5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4938 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Large-Scale Knowledge Resources, LKR 2008
Country/Territory	Japan
City	Tokyo
Period	2008/03/03 → 2008/03/05

Keywords

Bootstrap Method
Natural Language Processing
Sense Tagged Corpus
Word Sense Disambiguation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-78159-2_18

Cite this

Ker, S. J., Huang, C. R., Hong, J. F., Liu, S. Y., Jian, H. L., Su, I. L., & Hsieh, S. K. (2008). Design and prototype of a large-scale and fully sense-tagged corpus. In Large-Scale Knowledge Resources: Construction and Application - Third International Conference on Large-Scale Knowledge Resources, LKR 2008, Proceedings (pp. 186-193). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4938 LNAI). https://doi.org/10.1007/978-3-540-78159-2_18

Design and prototype of a large-scale and fully sense-tagged corpus. / Ker, Sue Jin; Huang, Chu Ren; Hong, Jia Fei et al.
Large-Scale Knowledge Resources: Construction and Application - Third International Conference on Large-Scale Knowledge Resources, LKR 2008, Proceedings. 2008. p. 186-193 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4938 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ker, SJ, Huang, CR, Hong, JF, Liu, SY, Jian, HL, Su, IL & Hsieh, SK 2008, Design and prototype of a large-scale and fully sense-tagged corpus. in Large-Scale Knowledge Resources: Construction and Application - Third International Conference on Large-Scale Knowledge Resources, LKR 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4938 LNAI, pp. 186-193, 3rd International Conference on Large-Scale Knowledge Resources, LKR 2008, Tokyo, Japan, 2008/03/03. https://doi.org/10.1007/978-3-540-78159-2_18

Ker SJ, Huang CR, Hong JF, Liu SY, Jian HL, Su IL et al. Design and prototype of a large-scale and fully sense-tagged corpus. In Large-Scale Knowledge Resources: Construction and Application - Third International Conference on Large-Scale Knowledge Resources, LKR 2008, Proceedings. 2008. p. 186-193. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-78159-2_18

Ker, Sue Jin ; Huang, Chu Ren ; Hong, Jia Fei et al. / Design and prototype of a large-scale and fully sense-tagged corpus. Large-Scale Knowledge Resources: Construction and Application - Third International Conference on Large-Scale Knowledge Resources, LKR 2008, Proceedings. 2008. pp. 186-193 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AU - Ker, Sue Jin

AU - Huang, Chu Ren

AU - Hong, Jia Fei

AU - Liu, Shi Yin

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AU - Su, I. Li

AU - Hsieh, Shu Kai

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AB - Sense tagged corpus plays a very crucial role to Natural Language Processing, especially on the research of word sense disambiguation and natural language understanding. Having a large-scale Chinese sense tagged corpus seems to be very essential, but in fact, such large-scale corpus is the critical deficiency at the current stage. This paper is aimed to design a large-scale Chinese full text sense tagged Corpus, which contains over 110,000 words. The Academia Sinica Balanced Corpus of Modern Chinese (also named Sinica Corpus) is treated as the tagging object, and there are 56 full texts extracted from this corpus. By using the N-gram statistics and the information of collocation, the preparation work for automatic sense tagging is planned by combining the techniques and methods of machine learning and the probability model. In order to achieve a highly precise result, the result of automatic sense tagging needs the touch of manual revising.

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Design and prototype of a large-scale and fully sense-tagged corpus

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Keywords

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