Diverse Topic Phrase Extraction through Latent Semantic Analysis

Authors:
Jilin Chen;Jun Yan;Benyu Zhang;Qiang Yang;Zheng Chen
Affiliations:
University of Minnesota, USA;Microsoft Research Asia, China;Microsoft Research Asia, China;Hong Kong University of Science and Technology, Hong Kong;Microsoft Research Asia, China
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

Citing 0
Cited 5

A novel clustering-based RSS aggregator

Proceedings of the 16th international conference on World Wide Web
Automatic labeling of multinomial topic models

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An online blog reading system by topic clustering and personalized ranking

ACM Transactions on Internet Technology (TOIT)
Event driven summarization for web videos

WSM '09 Proceedings of the first SIGMM workshop on Social media
Automatic labeling hierarchical topics

Proceedings of the 21st ACM international conference on Information and knowledge management

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.