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
Externally publishedYes
EventProceedings of the 9th Conference on Artificial Intelligence for Applications - Orlando, FL, USA
Duration: 1993 Mar 11993 Mar 5

Other

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

Fingerprint

Decision trees
Learning algorithms
Learning systems
Processing

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 Publ by IEEE.

Text classification and keyword extraction by learning decision trees. / Sakakibara, Yasubumi; Misue, Kazuo; Koshiba, Takeshi.

Proceedings of the Conference on Artificial Intelligence Applications. Publ by IEEE, 1993.

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

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. Publ by IEEE, Proceedings of the 9th Conference on Artificial Intelligence for Applications, Orlando, FL, USA, 93/3/1.
Sakakibara Y, Misue K, Koshiba T. Text classification and keyword extraction by learning decision trees. In Proceedings of the Conference on Artificial Intelligence Applications. Publ by IEEE. 1993
Sakakibara, Yasubumi ; Misue, Kazuo ; Koshiba, Takeshi. / Text classification and keyword extraction by learning decision trees. Proceedings of the Conference on Artificial Intelligence Applications. Publ by IEEE, 1993.
@inproceedings{b7b617c6e7954362b7cb1c29aa1479e3,
title = "Text classification and keyword extraction by learning decision trees",
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.",
author = "Yasubumi Sakakibara and Kazuo Misue and Takeshi Koshiba",
year = "1993",
language = "English",
isbn = "0818638400",
booktitle = "Proceedings of the Conference on Artificial Intelligence Applications",
publisher = "Publ by IEEE",

}

TY - GEN

T1 - Text classification and keyword extraction by learning decision trees

AU - Sakakibara, Yasubumi

AU - Misue, Kazuo

AU - Koshiba, Takeshi

PY - 1993

Y1 - 1993

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0027289832&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027289832&partnerID=8YFLogxK

M3 - Conference contribution

SN - 0818638400

BT - Proceedings of the Conference on Artificial Intelligence Applications

PB - Publ by IEEE

ER -