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Language Resources and Evaluation

, Volume 47, Issue 1, pp 127–149 | Cite as

A document is known by the company it keeps: neighborhood consensus for short text categorization

  • Gabriela Ramírez-de-la-RosaEmail author
  • Manuel Montes-y-Gómez
  • Thamar Solorio
  • Luis Villaseñor-Pineda
Original Paper
  • 359 Downloads

Abstract

During the last decades the Web has become the greatest repository of digital information. In order to organize all this information, several text categorization methods have been developed, achieving accurate results in most cases and in very different domains. Due to the recent usage of Internet as communication media, short texts such as news, tweets, blogs, and product reviews are more common every day. In this context, there are two main challenges; on the one hand, the length of these documents is short, and therefore, the word frequencies are not informative enough, making text categorization even more difficult than usual. On the other hand, topics are changing constantly at a fast rate, causing the lack of adequate amounts of training data. In order to deal with these two problems we consider a text classification method that is supported on the idea that similar documents may belong to the same category. Mainly, we propose a neighborhood consensus classification method that classifies documents by considering their own information as well as information about the category assigned to other similar documents from the same target collection. In particular, the short texts we used in our evaluation are news titles with an average of 8 words. Experimental results are encouraging; they indicate that leveraging information from similar documents helped to improve classification accuracy and that the proposed method is especially useful when labeled training resources are limited.

Keywords

Short text categorization Unlabeled information Prototype-based classification News titles 

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Gabriela Ramírez-de-la-Rosa
    • 1
    Email author
  • Manuel Montes-y-Gómez
    • 2
  • Thamar Solorio
    • 1
  • Luis Villaseñor-Pineda
    • 2
  1. 1.Department of Computer and Information SciencesUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.Department of Computational SciencesNational Institute for Astrophysics, Optics and ElectronicsPueblaMexico

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