Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Text Representation

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_420

Definition

Text representation is one of the fundamental problems in text mining and Information Retrieval (IR). It aims to numerically represent the unstructured text documents to make them mathematically computable. For a given set of text documents D = {di, i = 1, 2, … , n}, where each di stands for a document, the problem of text representation is to represent each di of D as a point si in a numerical space S, where the distance/similarity between each pair of points in space S is well defined.

Historical Background

Mining the unstructured text data has attracted much attention of researchers in different areas due to its great industrial and commercial application potentials. A fundamental problem of text mining is how to represent the text documents to make them mathematically computable. Various text representation strategies have been proposed in the past decades for different application purposes such as text categorization, novelty detection and Information Retrieval (IR) [5]...

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Recommended Reading

  1. 1.
    Alter O, Brown PO, Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA. 2000;97(18):10101–6.CrossRefGoogle Scholar
  2. 2.
    Daniel J, James HM. Speech and language processing: an introduction to natural language processing, computational linguistics, and speech processing. Englewood Cliffs: Prentice-Hall; 2000.Google Scholar
  3. 3.
    Deerwester S, Dumais ST, Landauer TK, Furnas GW, Harshman RA. Indexing by latent semantic analysis. J Soc Inf Sci. 1990;41(6):391–407.CrossRefGoogle Scholar
  4. 4.
    Gerard SA. Theory of indexing. Philadelphia: Society for Industrial Mathematics; 1987.Google Scholar
  5. 5.
    Gerard S, Michael J. Introduction to Modern Information Retrieval. New York: McGraw-Hill; 1983.zbMATHGoogle Scholar
  6. 6.
    Thomas H. Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR conference on Research and Development in Information Retrieval; 1999. p. 50–7.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Microsoft Research AsiaHaidianChina

Section editors and affiliations

  • Zheng Chen
    • 1
  1. 1.Microsoft Research AsiaMicrosoft CorporationBeijingChina