Exploiting noun phrases and semantic relationships for text document clustering
Information Sciences: an International Journal
A Semi-supervised Topic-Driven Approach for Clustering Textual Answers to Survey Questions
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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Recent work has shown that ontology is useful to improve the performance of information retrieval, especially in biomedical literatures. The method of ontology-based can solve synonym problems. In this paper, we propose a new frame for genomic information retrieval based on UMLS. In our frame, Genomic information retrieval includes three processes: first, documents were indexed based UMLS, which means documents were represented by concepts, besides, the concept weight was re-calculated combined with similarity between concepts. Second, documents were clustered using fuzzy c-means method. At last cluster language model is utilized for information retrieval. Our method can solve partly synonymy and polysemy problems. The new method is evaluated on TREC 2004/05 Genomics Track collections. Experiments show that the retrieval performance is greatly improved by the new method compared with the basic language model.