Keyphrase extraction with sequential pattern mining

Qingren Wang, Victor S. Sheng, Xindong Wu

Research output: Contribution to conferencePaper

6 Scopus citations

Abstract

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
Pages5003-5004
Number of pages2
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017

Conference

Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period02/4/1702/10/17

Cite this

Wang, Q., Sheng, V. S., & Wu, X. (2017). Keyphrase extraction with sequential pattern mining. 5003-5004. Paper presented at 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, United States.