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Encyclopedia of Database Systems pp 4088–4092Cite as

Text Mining

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Synonyms

Knowledge discovery in text (KDT)

Definition

Text mining is the art of data mining from text data collections. The goal is to discover knowledge (or information, patterns) from text data, which are unstructured or semi-structured. It is a subfield of Data Mining (DM), which is also known as Knowledge Discovery in Databases (KDD). KDD is to discover knowledge from various data sources, including text data, relational databases, Web data, user log data, etc. Text Mining is also related to other research fields, including Machine Learning (ML), Information Retrieval (IR), Natural Language Processing (NLP), Information Extraction (IE), Statistics, Pattern Recognition (PR), Artificial Intelligence (AI), etc.

Historical Background

The phrase of Knowledge Discovery in Databases (KDD) was first used at 1st KDD workshop in 1989. Marti Hearst [4] first used the term of text data mining (TDM) and differentiated it with other concepts such as information retrieval and natural language...

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Recommended Reading

  1. Andreas H, Andreas N, Gerhard P. A brief survey of text mining. J Computat Linguistics Lang Technol. 2005;20(1):19–62.

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  2. Bing L. Web data mining: exploring hyperlinks contents and usage data. Berlin: Springer; 2007. p. 411–47.

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  3. Dipanjan D, Martins AFT. A survey on automatic text summarization. Literature survey for the language and statistics II course at Carnegie Mellon University; November. 2007.

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  4. Hearst M Untangling text data mining. In: Proceedings of the 27th Annual Meeting of the Associate for Computational Linguistics; 1999.

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  5. Informative and indicative summarization. Available at: http://www1.cs.columbia.edu/~min/papers/sigirDuc01/node2.html.

  6. Liebman M. Bioinformatics: an editorial perspective. Available at: http://www.netsci.org/Science/Bioinform/feature01.html.

  7. Usama F, Gregory P-S, Padhraic S. From data mining to knowledge discovery in databases. AI Mag. 1996;17(3):37–54.

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  8. Wayne CL. Multilingual topic detection and tracking: successful research enabled by corpora and evaluation. In: Proceedings of the 27th Annual Meeting of the Associate for Computational Linguistics; 2000.

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  9. Witten IH. Text mining. In: Singh MP, editor. Practical handbook of internet computing. Boca Raton: Chapman and Hall/CRC Press; 2005. p. 14-1–14-22.

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Author information

Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Yanli Cai

  2. Microsoft Research Asia, Beijing, China

    Jian-Tao Sun

Authors
  1. Yanli Cai
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  2. Jian-Tao Sun
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Corresponding author

Correspondence to Yanli Cai .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Microsoft Research Asia, Microsoft Corporation, Beijing, Haidian, China

    Zheng Chen

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Cite this entry

Cai, Y., Sun, JT. (2018). Text Mining. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_418

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