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EMNLP
2010

Automatic Keyphrase Extraction via Topic Decomposition

8 years 1 months ago
Automatic Keyphrase Extraction via Topic Decomposition
Existing graph-based ranking methods for keyphrase extraction compute a single importance score for each word via a single random walk. Motivated by the fact that both documents and words can be represented by a mixture of semantic topics, we propose to decompose traditional random walk into multiple random walks specific to various topics. We thus build a Topical PageRank (TPR) on word graph to measure word importance with respect to different topics. After that, given the topic distribution of the document, we further calculate the ranking scores of words and extract the top ranked ones as keyphrases. Experimental results show that TPR outperforms state-of-the-art keyphrase extraction methods on two datasets under various evaluation metrics.
Zhiyuan Liu, Wenyi Huang, Yabin Zheng, Maosong Sun
Added 11 Feb 2011
Updated 11 Feb 2011
Type Journal
Year 2010
Where EMNLP
Authors Zhiyuan Liu, Wenyi Huang, Yabin Zheng, Maosong Sun
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