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Guiding practical text classification framework to optimal state in multiple domains

Choi, Sung Pil ; Myaeng, Sung - Hyon ; Cho, Hyun - Yang

KSII Transactions on Internet and Information Systems, June, 2009, Vol.3(3), p.285(23) [Periódico revisado por pares]

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  • Título:
    Guiding practical text classification framework to optimal state in multiple domains
  • Autor: Choi, Sung Pil ; Myaeng, Sung - Hyon ; Cho, Hyun - Yang
  • Assuntos: Data Mining -- Research ; Data Mining -- Conferences, Meetings And Seminars ; Software Engineering -- Analysis
  • É parte de: KSII Transactions on Internet and Information Systems, June, 2009, Vol.3(3), p.285(23)
  • Descrição: 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. Keywords: Document classification, test bed, machine learning, text categorization, feature selection, text mining, lemmatization
  • Idioma: English

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