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An Efficient Concept-Based Mining Model for Enhancing Text Clustering

An Efficient Concept-Based Mining Model for Enhancing Text Clustering. Presenter : JHOU, YU-LIANG Authors :Shady Shehata , Fakhri Karray , Mohamed S. Kamel , Fellow 2012 , IEEE. Outlines. Motivation Objectives Methodology Evaluation Conclusions Comments. Motivation.

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An Efficient Concept-Based Mining Model for Enhancing Text Clustering

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  1. An Efficient Concept-Based Mining Model for Enhancing Text Clustering Presenter : JHOU, YU-LIANGAuthors :Shady Shehata, FakhriKarray, Mohamed S. Kamel, Fellow2012, IEEE

  2. Outlines • Motivation • Objectives • Methodology • Evaluation • Conclusions • Comments

  3. Motivation • In text mining ,the term frequency is computed to explore the importance of the term in document. • However, two terms can have the same frequency in documents, but one term contributes more to the meaning of its sentences than the other term.

  4. Objectives Using Concept-Based Mining Model for Text Clustering , improve the clustering quality.

  5. MethodologyConcept-Based Mining Model

  6. MethodologyCONCEPT-BASED MINING MODEL Ex: a concept cwhich appears twice in document d in the first and the secondsentences The concept c appears fivetimes in the verb argument structures of the first sentence s 1 , and three times in the verb argument structures of the second sentence s 2 . ans : ctf value = (5+3)/2=4

  7. MethodologyCorpus-Based Concept Analysis Algorithm

  8. MethodologyExample of Conceptual Term Frequency . [ARG0 Texas and Australia researchers] have [TARGET created] [ARG1 industry-ready sheets of materials made from nanotubes that could lead to the development of artificial muscles]. [ARG1 materials] [TARGET made ] [ARG2 from nanotubes that could lead to the development of artificial muscles]. [ARG1 nanotubes] [R-ARG1 that] [ARGM-MOD could] [TARGET lead] [ARG2 to the development of artificial muscles].

  9. MethodologyExample of Conceptual Term Frequency 1. First verb argument structure for the verb created: . [ARG0 Texas and Australia researchers] . [TARGET created] . [ARG1 industry-ready sheets of materials made from nanotubes that could lead to the development of artificial muscles]. 2. Second verb argument structure for the verb made: . [ARG1 materials] . [TARGET made] . [ARG2 from nanotubes that could lead to the development of artificial muscles]. 3. Third verb argument structure for the verb lead: . [ARG1 nanotubes] . [R-ARG1 that] . [ARGM-MOD could] . [TARGET lead] . [ARG2 to the development of artificial muscles].

  10. MethodologyExample of Conceptual Term Frequency 1. Concepts in the first verb argument structure of the verb created: . Texas Australia researchers . created . industry-ready sheets materials nanotubes lead development artificial muscles 2. Concepts in the second verb argument structure of the verb made: . materials . nanotubes lead development artificial muscles 3. Concepts in the third verb argument structure of the verb lead: . nanotubes . lead . development artificial muscles.

  11. MethodologyExample of Conceptual Term Frequency

  12. MethodologyConcept-Based Similarity Measure

  13. Experimental Result

  14. Experimental Result

  15. Experimental Result

  16. Experimental Result

  17. Conclusions The new approach enhance text clustering quality.

  18. Comments Advantages Improve the text clustering quality. Applications -Concept-based mining model -Conceptual term frequency

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