Semantic Mapping for Related Term Identification
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.
KeywordsVector Space Model Latent Semantic Analysis Multidimensional Scaling Related Term Identification
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