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Tapping the power of text mining

Authors:

Weiguo Fan,

Linda Wallace,

Stephanie Rich,

Zhongju ZhangAuthors Info & Claims

Communications of the ACM, Volume 49, Issue 9

Pages 76 - 82

https://doi.org/10.1145/1151030.1151032

Published: 01 September 2006 Publication History

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Abstract

Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.

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Jo IHan SHou LMoon SKim J(2025)Developing and Evaluating a Classification Model for Construction Defect Control: A Text Mining and Ensemble Learning ApproachJournal of Management in Engineering10.1061/JMENEA.MEENG-629641:2Online publication date: Mar-2025
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Index Terms

Tapping the power of text mining

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Text mining is an increasingly important research field because of the necessity of obtaining knowledge from the enormous number of text documents available, especially on the Web. Text mining and data mining, both included in the field of information ...

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Published In

Communications of the ACM Volume 49, Issue 9

Privacy and security in highly dynamic systems

September 2006

100 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/1151030

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2006

Published in CACM Volume 49, Issue 9

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Abstract

References

Cited By

Index Terms

Recommendations

Text association mining with cross-sentence inference, structure-based document model and multi-relational text mining

Mining Text Using Keyword Distributions

Mining Text Data: Special Features and Patterns

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