Social Semantics And Its Evaluation By Means of Closed Topic Models: An SVM-Classification Approach Using Semantic Feature Replacement By Topic Generalization

Title: Social Semantics And Its Evaluation By Means of Closed Topic Models: An SVM-Classification Approach Using Semantic Feature Replacement By Topic Generalization
Authors: Ulli Waltinger, Alexander Mehler, and Rüdiger Gleim
Pub/Conf:  GSCL-Conference 2009

Abstract:
Text categorization is a fundamental part in many NLP applications. In general, the Vector Space Model, the Latent Semantic Analysis and Support Vector Machine implementation have been successfully applied within this area. However, feature extraction is the most challenging task when conducting categorization experiments. Moreover, sensitive feature reduction is needed in order to reduce time and space complexity especially when deal with singular value decomposition or larger sized text collections. In this paper we examine the task of feature reduction by means of closed topic models. We propose a feature replacement technique conducting a topic generalization comprising user generated concepts of a social ontology. Derived feature concepts are then subsequently used to enhance and replace existing features gaining a minimum representation of twenty social concepts. We examine the e ect of each step in the classi cation process using a large corpus of 29,086 texts comprising 30 di erent categories. In addition, we o ffer an easy-to-use web interface as part of the eHumanities Desktop in order to test the proposed classifi ers.

BibTeX:

@inproceedings{1904121,

  author       = {Waltinger, Ulli and Mehler, Alexander and Gleim, R{\"u}diger},
  language     = {English},
  series       = {Proceedings of the GSCL-Conference 2009},
  title        = {Social Semantics And Its Evaluation By Means of Closed Topic Models: 
                 An SVM-Classification Approach Using Semantic Feature Replacement 
                 By Topic Generalization},
  year         = {2009},
}

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