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Textmining without document context.

Abstract : We consider a challenging clustering task: the clustering of multi-word terms without document co-occurrence information in order to form coherent groups of topics. For this task, we developed a methodology taking as input multi-word terms and lexico-syntactic relations between them. Our clustering algorithm, named CPCL is implemented in the TermWatch system. We compared CPCL to other existing clustering algorithms, namely hierarchical and partitioning (k-means, k-medoids). This out-of-context clustering task led us to adapt multi-word term representation for statistical methods and also to refine an existing cluster evaluation metric, the editing distance in order to evaluate the methods. Evaluation was carried out on a list of multi-word terms from the genomic field which comes with a hand built taxonomy. Results showed that while k-means and k-medoids obtained good scores on the editing distance, they were very sensitive to term length. CPCL on the other hand obtained a better cluster homogeneity score and was less sensitive to term length. Also, CPCL showed good adaptability for handling very large and sparse matrices.
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Contributor : Fidelia Ibekwe <>
Submitted on : Wednesday, November 2, 2011 - 7:36:22 PM
Last modification on : Thursday, May 28, 2020 - 7:52:02 PM
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Eric Sanjuan, Fidelia Ibekwe-Sanjuan. Textmining without document context.. Information Processing and Management, Elsevier, 2006, 42 (6), pp.1532-1552. ⟨10.1016/j.ipm.2006.03.017⟩. ⟨hal-00636111⟩



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