Text classification and keyword extraction by learning decision trees

Yasubumi Sakakibara, Kazuo Misue, Takeshi Koshiba

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

8 Citations (Scopus)

Abstract

In this paper, we propose a completely new approach to the problem of text classification and automatic keyword extraction by using machine learning techniques. We introduce a class of representations for classifying text data based on decision trees, and present an algorithm for learning it inductively. Our algorithm has the following features: it does not need any natural language processing technique, and it is robust for noisy data. We show that our learning algorithm can be used for automatic extraction of keywords for text retrieval and automatic text categorization. We also demonstrate some experimental results using our algorithm.

Original languageEnglish
Title of host publicationProceedings of the Conference on Artificial Intelligence Applications
PublisherPubl by IEEE
Number of pages1
ISBN (Print)0818638400
Publication statusPublished - 1993 Jan 1
EventProceedings of the 9th Conference on Artificial Intelligence for Applications - Orlando, FL, USA
Duration: 1993 Mar 11993 Mar 5

Publication series

NameProceedings of the Conference on Artificial Intelligence Applications

Other

OtherProceedings of the 9th Conference on Artificial Intelligence for Applications
CityOrlando, FL, USA
Period93/3/193/3/5

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ASJC Scopus subject areas

  • Software

Cite this

Sakakibara, Y., Misue, K., & Koshiba, T. (1993). Text classification and keyword extraction by learning decision trees. In Proceedings of the Conference on Artificial Intelligence Applications (Proceedings of the Conference on Artificial Intelligence Applications). Publ by IEEE.