کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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515822 | 867099 | 2006 | 21 صفحه PDF | دانلود رایگان |
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.
Journal: Information Processing & Management - Volume 42, Issue 6, December 2006, Pages 1532–1552