Semantic Mapping for Related Term Identification

  • Rafael E. Banchs
Conference paper

DOI: 10.1007/978-3-642-00382-0_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5449)
Cite this paper as:
Banchs R.E. (2009) Semantic Mapping for Related Term Identification. In: Gelbukh A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2009. Lecture Notes in Computer Science, vol 5449. Springer, Berlin, Heidelberg

Abstract

In this work, we explore the combined use of latent semantic analysis (LSA) and multidimensional scaling (MDS) for identifying related concepts and terms. We approach the problem of related term identification by constructing low-dimensional embeddings where related terms are clustered together, and such clusters are spatially arranged according to the semantic relationships among the terms they include. In this work, we demonstrate the proposed methodology for a specific part-of-speech (verbs) of the Spanish language, by using dictionary-based definitions. We also comment on the future use of this experimental framework in the context of other natural language processing tasks such as opinion mining, topic detection and automatic summarization.

Keywords

Vector Space Model Latent Semantic Analysis Multidimensional Scaling Related Term Identification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rafael E. Banchs
    • 1
  1. 1.Barcelona Media Innovation CentreBarcelonaSpain

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