Mining text using keyword distributions

Ronen Feldman; Ido Dagan; Haym Hirsh

doi:10.1023/A:1008623632443

Mining text using keyword distributions

Ronen Feldman^*, Ido Dagan, Haym Hirsh

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

107 Scopus citations

Abstract

Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form. This paper describes the KDT system for Knowledge Discovery in Text, in which documents are labeled by keywords, and knowledge discovery is performed by analyzing the co-occurrence frequencies of the various keywords labeling the documents. We show how this keyword-frequency approach supports a range of KDD operations, providing a suitable foundation for knowledge discovery and exploration for collections of unstructured text.

Original language	English
Pages (from-to)	281-300
Number of pages	20
Journal	Journal of Intelligent Information Systems
Volume	10
Issue number	3
DOIs	https://doi.org/10.1023/A:1008623632443
State	Published - May 1998
Externally published	Yes

Bibliographical note

Funding Information:
This research was supported by NSF grant IRI-9509819 and by grant 8615-1-96 from the Israeli Ministry of Science. The authors would like to thank the reviewers for helpful comments given on drafts of this paper.

Keywords

Data mining
Distribution comparison
Text categorization
Text mining
Trend analysis

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10.1023/A:1008623632443

Cite this

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title = "Mining text using keyword distributions",

abstract = "Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form. This paper describes the KDT system for Knowledge Discovery in Text, in which documents are labeled by keywords, and knowledge discovery is performed by analyzing the co-occurrence frequencies of the various keywords labeling the documents. We show how this keyword-frequency approach supports a range of KDD operations, providing a suitable foundation for knowledge discovery and exploration for collections of unstructured text.",

keywords = "Data mining, Distribution comparison, Text categorization, Text mining, Trend analysis",

author = "Ronen Feldman and Ido Dagan and Haym Hirsh",

note = "Funding Information: This research was supported by NSF grant IRI-9509819 and by grant 8615-1-96 from the Israeli Ministry of Science. The authors would like to thank the reviewers for helpful comments given on drafts of this paper.",

year = "1998",

month = may,

doi = "10.1023/A:1008623632443",

language = "אנגלית",

volume = "10",

pages = "281--300",

journal = "Journal of Intelligent Information Systems",

issn = "0925-9902",

publisher = "Springer Netherlands",

number = "3",

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T1 - Mining text using keyword distributions

AU - Feldman, Ronen

AU - Dagan, Ido

AU - Hirsh, Haym

N1 - Funding Information: This research was supported by NSF grant IRI-9509819 and by grant 8615-1-96 from the Israeli Ministry of Science. The authors would like to thank the reviewers for helpful comments given on drafts of this paper.

PY - 1998/5

Y1 - 1998/5

N2 - Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form. This paper describes the KDT system for Knowledge Discovery in Text, in which documents are labeled by keywords, and knowledge discovery is performed by analyzing the co-occurrence frequencies of the various keywords labeling the documents. We show how this keyword-frequency approach supports a range of KDD operations, providing a suitable foundation for knowledge discovery and exploration for collections of unstructured text.

AB - Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form. This paper describes the KDT system for Knowledge Discovery in Text, in which documents are labeled by keywords, and knowledge discovery is performed by analyzing the co-occurrence frequencies of the various keywords labeling the documents. We show how this keyword-frequency approach supports a range of KDD operations, providing a suitable foundation for knowledge discovery and exploration for collections of unstructured text.

KW - Data mining

KW - Distribution comparison

KW - Text categorization

KW - Text mining

KW - Trend analysis

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Mining text using keyword distributions

Abstract

Bibliographical note

Keywords

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