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Effective and Efficient Classification of Topically-Enriched Domain-Specific Text Snippets: The TETSC Method

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  • Marco Spruit

    (Department of Information and Computer Sciences, Utrecht University, Utrecht, The Netherlands)

  • Bas Vlug

    (Department of Information and Computer Sciences, Utrecht University, Utrecht, The Netherlands)

Abstract

Due to the explosive growth in the amount of text snippets over the past few years and their sparsity of text, organizations are unable to effectively and efficiently classify them, missing out on business opportunities. This paper presents TETSC: the Topically-Enriched Text Snippet Classification method. TETSC aims to solve the classification problem for text snippets in any domain. TETSC recognizes that there are different types of text snippets and, therefore, allows for stop word removal, named-entity recognition, and topical enrichment for the different types of text snippets. TETSC has been implemented in the production systems of a personal finance organization, which resulted in a classification error reduction of over 21%. Highlights: The authors create the TETSC method for classifying topically-enriched text snippets; the authors differentiate between different types of text snippets; the authors show a successful application of Named-Entity Recognition to text snippets; using multiple enrichment strategies appears to reduce effectivity.

Suggested Citation

  • Marco Spruit & Bas Vlug, 2015. "Effective and Efficient Classification of Topically-Enriched Domain-Specific Text Snippets: The TETSC Method," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 6(3), pages 1-17, July.
    • Handle: RePEc:igg:jsds00:v:6:y:2015:i:3:p:1-17
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