Learning by examples: Identifying key concepts from text using pre-defined inputs

Yi Fang Brook Wu, Quanzhi Li, Xin Chen, Razvan Stefan Bot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This article describes a keyphrase identification program (KIP) which extracts document key concepts by using sample human keyphrases. KIP considers the composition of a keyphrase. The more keywords a phrase contains and the more significant these keywords are, the more likely this phrase is a keyphrase. KIP first populates its database using manually identified keyphrases and keywords; it then checks the composition of all identified noun phrases, looks up the database and calculates scores for all these noun phrases; the ones having higher scores will be extracted as keyphrases. KIP's learning function can enrich the database by automatically adding new keyphrases to the database. Consequently, the database will grow gradually and the system performance will be improved. The results from our small-scale preliminary experiments show that KIP is effective in extracting document keyphrases and its learning function is useful.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 International Conference on Artificial Intelligence, ICAI'05
Pages826-832
Number of pages7
Volume2
StatePublished - Dec 1 2005
Event2005 International Conference on Artificial Intelligence, ICAI'05 - Las Vegas, NV, United States
Duration: Jun 27 2005Jun 30 2005

Other

Other2005 International Conference on Artificial Intelligence, ICAI'05
Country/TerritoryUnited States
CityLas Vegas, NV
Period6/27/056/30/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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