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A Concept-based Model for Enhancing Text Categorization

This study presents a novel concept-based model for text categorization, addressing the limitations of traditional techniques that rely primarily on word and phrase analysis. The proposed model analyzes terms at both the sentence and document levels, allowing for deeper semantic understanding. Through Natural Language Processing and statistical analysis, this research demonstrates that the new model significantly improves categorization results compared to conventional methods. It bridges the gap between various disciplines and holds potential applications in document and web categorization.

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A Concept-based Model for Enhancing Text Categorization

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  1. A Concept-based Model for Enhancing Text Categorization Presenter : Jiang-Shan Wang Authors : Shady Shehata, Fakhri Karray, Mohamed Kamel 國立雲林科技大學 National Yunlin University of Science and Technology SIGKDD 2008

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation • Most of text categorization techniques are based on word and/or phrase analysis of the text. • However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. • Example ： • electronic techniques • defense effort

  4. Objective To propose a new concept-based model that analyzes terms on the sentence and document levels.

  5. Methods - Overview

  6. Methods – Natural Language Processing We have noted how some electronic techniques, developed for the defense effort, have eventually been used in commerce and industry. [ARG0 We] [TARGET noted] [ARG1 how some electronic techniques developed for the defense effort have eventually been used in commerce and industry]. We have noted how [ARG1 some electronic techniques] [TARGET developed] [ARGM-PNC for the defense effort] have eventually been used in commerce and industry. We have noted how [ARG1 some electronic techniques developed for the defense effort] have [ARGM-TMP eventually] been [TARGET used] [ARGM-LOC in commerce and industry].

  7. Methods – Statistical Analyzer

  8. Methods – Conceptual Ontological Graph

  9. Methods – Concept Extractor

  10. Experiments

  11. Conclusion This work bridges the gap between natural language processing and text categorization disciplines. The quality of the categorization results by the proposed model surpasses that of traditional approaches significantly.

  12. Comments • Advantage • Considering about sentence semantics for text categorization. • Drawback • . • Application • Text categorization. • Document categorization • Web document categorization.

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