Comparison of Word Embeddings from Different Knowledge Graphs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10318)

Abstract

The paper focuses on the manipulation of a WordNet-based knowledge graph by adding, changing and combining various semantic relations. This is done in the context of measuring similarity and relatedness between words, based on word embedding representations trained on a pseudo corpus generated from the knowledge graph. The UKB tool is used for generating pseudo corpora that are then used for learning word embeddings. The results from the performed experiments show that the addition of more relations generally improves performance along both dimensions – similarity and relatedness. In line with previous research, our survey confirms that paradigmatic relations predominantly improve similarity, while syntagmatic relations benefit relatedness scores.

Keywords

Knowledge-based word embedding Semantic relations Similarity Association 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Information and Communication Technologies, BASSofiaBulgaria

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