KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Guiding Practical Text Classification Framework to Optimal State in Multiple Domains

  • Choi, Sung-Pil (Department of Information Technology Research, KISTI) ;
  • Myaeng, Sung-Hyon (School of Engineering, Information and Communications University) ;
  • Cho, Hyun-Yang (Library & Information Science Department, Kyonggi University)
  • Published : 2009.06.25
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Abstract

This paper introduces DICE, a Domain-Independent text Classification Engine. DICE is robust, efficient, and domain-independent in terms of software and architecture. Each module of the system is clearly modularized and encapsulated for extensibility. The clear modular architecture allows for simple and continuous verification and facilitates changes in multiple cycles, even after its major development period is complete. Those who want to make use of DICE can easily implement their ideas on this test bed and optimize it for a particular domain by simply adjusting the configuration file. Unlike other publically available tool kits or development environments targeted at general purpose classification models, DICE specializes in text classification with a number of useful functions specific to it. This paper focuses on the ways to locate the optimal states of a practical text classification framework by using various adaptation methods provided by the system such as feature selection, lemmatization, and classification models.

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References

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