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

  • Authors:

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

  • Affiliations:

  • Department of Computer and Information Science, Soochow University, Taiwan;Institute of Linguistics, Academia Sinica, Taiwan;Graduate Institute of Linguistics, National Taiwan University, Taiwan;Department of Computer and Information Science, Soochow University, Taiwan;Department of Computer and Information Science, Soochow University, Taiwan;Institute of Linguistics, Academia Sinica, Taiwan;Department of English, National Taiwan Normal University, Taiwan

  • Venue:
  • LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
  • Year:
  • 2008

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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.