Text mining at the term level

Ronen Feldman; Moshe Gresko; Yakkov Kinar; Yehuda Lindell; Oren Liphstat; Martin Rajman; Yonatan Schler; Oren Zamir

doi:10.1007/bfb0094806

Text mining at the term level

Ronen Feldman, Moshe Gresko, Yakkov Kinar, Yehuda Lindell, Oren Liphstat, Martin Rajman, Yonatan Schler, Oren Zamir

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

87 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. Previous work in text mining focused at the word or the tag level. This paper presents an approach to performing text mining at the term level. The mining process starts by preprocessing the document collection and extracting terms from the documents. Each document is then represented by a set of terms and annotations characterizing the document. Terms and additional higher-level entities are then organized in a hierarchical taxonomy. In this paper we will describe the Term Extraction module of the Document Explorer system, and provide experimental evaluation performed on a set of 52,000 documents published by Reuters in the years 1995-1996.

Original language	English
Title of host publication	Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings
Editors	Jan M. Zytkow, Mohamed Quafafou
Publisher	Springer Verlag
Pages	65-73
Number of pages	9
ISBN (Print)	3540650687, 9783540650683
DOIs	https://doi.org/10.1007/bfb0094806
State	Published - 1998
Externally published	Yes
Event	2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998 - Nantes, France Duration: 23 Sep 1998 → 26 Sep 1998

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1510
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998
Country/Territory	France
City	Nantes
Period	23/09/98 → 26/09/98

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1998.

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10.1007/bfb0094806

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Feldman, R., Gresko, M., Kinar, Y., Lindell, Y., Liphstat, O., Rajman, M., Schler, Y., & Zamir, O. (1998). Text mining at the term level. In J. M. Zytkow, & M. Quafafou (Eds.), Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings (pp. 65-73). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1510). Springer Verlag. https://doi.org/10.1007/bfb0094806

Feldman, Ronen ; Gresko, Moshe ; Kinar, Yakkov et al. / Text mining at the term level. Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings. editor / Jan M. Zytkow ; Mohamed Quafafou. Springer Verlag, 1998. pp. 65-73 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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. Previous work in text mining focused at the word or the tag level. This paper presents an approach to performing text mining at the term level. The mining process starts by preprocessing the document collection and extracting terms from the documents. Each document is then represented by a set of terms and annotations characterizing the document. Terms and additional higher-level entities are then organized in a hierarchical taxonomy. In this paper we will describe the Term Extraction module of the Document Explorer system, and provide experimental evaluation performed on a set of 52,000 documents published by Reuters in the years 1995-1996.",

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Feldman, R, Gresko, M, Kinar, Y, Lindell, Y, Liphstat, O, Rajman, M, Schler, Y & Zamir, O 1998, Text mining at the term level. in JM Zytkow & M Quafafou (eds), Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1510, Springer Verlag, pp. 65-73, 2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998, Nantes, France, 23/09/98. https://doi.org/10.1007/bfb0094806

Text mining at the term level. / Feldman, Ronen; Gresko, Moshe; Kinar, Yakkov et al.
Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings. ed. / Jan M. Zytkow; Mohamed Quafafou. Springer Verlag, 1998. p. 65-73 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1510).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Feldman, Ronen

AU - Gresko, Moshe

AU - Kinar, Yakkov

AU - Lindell, Yehuda

AU - Liphstat, Oren

AU - Rajman, Martin

AU - Schler, Yonatan

AU - Zamir, Oren

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Feldman R, Gresko M, Kinar Y, Lindell Y, Liphstat O, Rajman M et al. Text mining at the term level. In Zytkow JM, Quafafou M, editors, Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings. Springer Verlag. 1998. p. 65-73. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/bfb0094806

Text mining at the term level

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