Welcome to the InfoSci Platform

Reference Hub

This research has been cited in:

Article
Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patientsTelematics and Informatics10.1016/j.tele.2018.04.002
Article
Bridging the information gap of disaster responders by optimizing data selection using cost and qualityComputers & Geosciences10.1016/j.cageo.2018.06.002
Chapter
Bridging the Information Gap: Mapping Data Sets on Information Needs in the Preparedness and Response PhaseTechnologies for Development10.1007/978-3-319-91068-0_18

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

Marco Spruit, Bas Vlug

Source Title: International Journal of Strategic Decision Sciences (IJSDS)6(3)

ISSN: 1947-8569|EISSN: 1947-8577|EISBN13: 9781466677555|DOI: 10.4018/IJSDS.2015070101

Cite Article Cite Article

MLA

Spruit, Marco, and Bas Vlug. "Effective and Efficient Classification of Topically-Enriched Domain-Specific Text Snippets: The TETSC Method." IJSDS vol.6, no.3 2015: pp.1-17. http://doi.org/10.4018/IJSDS.2015070101

APA

Spruit, M. & Vlug, B. (2015). Effective and Efficient Classification of Topically-Enriched Domain-Specific Text Snippets: The TETSC Method. International Journal of Strategic Decision Sciences (IJSDS), 6(3), 1-17. http://doi.org/10.4018/IJSDS.2015070101

Chicago

Spruit, Marco, and Bas Vlug. "Effective and Efficient Classification of Topically-Enriched Domain-Specific Text Snippets: The TETSC Method," International Journal of Strategic Decision Sciences (IJSDS) 6, no.3: 1-17. http://doi.org/10.4018/IJSDS.2015070101

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

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.