Keyphrase extraction with sequential pattern mining

Qingren Wang, Victor S. Sheng, Xindong Wu

Research output: Contribution to conferencePaperpeer-review

7 Scopus citations


Existing studies show that extracting a complete keyphrase candidate set is the first and crucial step to extract high quality keyphrases from documents. Based on a common sense that words do not repeatedly appear in an effective keyphrase, we propose a novel algorithm named KCSP for document-specific keyphrase candidate search using sequential pattern mining with gap constraints, which only needs to scan a document once and automatically specifies appropriate gap constraints for words without users' participation. The experimental results confirm that it helps improve the quality of keyphrase extraction.

Original languageEnglish
Number of pages2
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017


Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco

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