Sixth International Conference on Data Mining (ICDM'06) (2006)
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.61
Jilin Chen , University of Minnesota, USA
Jun Yan , Microsoft Research Asia, China
Benyu Zhang , Microsoft Research Asia, China
Qiang Yang , Hong Kong University of Science and Technology, Hong Kong
Zheng Chen , Microsoft Research Asia, China
We propose a novel algorithm for extracting diverse topic phrases in order to provide summary for large corpora. Previous works often ignore the importance of diversity and thus extract phrases crowded on some hot topics while failing to cover other less obvious but important topics. We solve this problem through document re-weighting and phrase diversification by using latent semantic analysis (LSA). Experiments on various datasets show that our new algorithm can improve relevance as well as diversity over different topics for topic phrase extraction problems.
B. Zhang, Q. Yang, J. Yan, Z. Chen and J. Chen, "Diverse Topic Phrase Extraction through Latent Semantic Analysis," Sixth International Conference on Data Mining (ICDM'06)(ICDM), Hong Kong, 2006, pp. 834-838.