Informativeness-based keyword extraction from short documents

Mika Timonen, Timo Toivanen, Y. Teng, C. Cheng, L. He

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

9 Citations (Scopus)


With the rise of user created content on the Internet, the focus of text mining has shifted. Twitter messages and product descriptions are examples of new corpora available for text mining. Keyword extraction, user modeling and text categorization are all areas that are focusing on utilizing this new data. However, as the documents within these corpora are considerably shorter than in the traditional cases, such as news articles, there are also new challenges. In this paper, we focus on keyword extraction from documents such as event and product descriptions, and movie plot lines that often hold 30 to 60 words. We propose a novel unsupervised keyword extraction approach called Informativeness-based Keyword Extraction (IKE) that uses clustering and three levels of word evaluation to address the challenges of short documents. We evaluate the performance of our approach by using manually tagged test sets and compare the results against other keyword extrac- tion methods, such as CollabRank, KeyGraph, Chi-squared, and TF-IDF. We also evaluate the precision and effectiveness of the extracted keywords for user modeling and recommendation and report the results of all approaches. In all of the experiments IKE out-performs the competition
Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Knowledge Discovery and Information Retrieval
Subtitle of host publicationSSTM 2012
ISBN (Print)978-989-8565-29-7
Publication statusPublished - 2012
MoE publication typeA4 Article in a conference publication
Event4th International Conference on Knowledge Discovery and Information Retrieval, KIDR 2012 - Barcelona, Spain
Duration: 4 Oct 20127 Oct 2012


Conference4th International Conference on Knowledge Discovery and Information Retrieval, KIDR 2012


  • Keyword extraction
  • machine learning
  • short documents
  • term weighting
  • text mining


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