Graph-based exploration and clustering analysis of semantic spaces
Veremyev, A., Semenov, A., Pasiliao, E. L., & Boginski, V. (2019). Graph-based exploration and clustering analysis of semantic spaces. Applied Network Science, 4, Article 104. https://doi.org/10.1007/s41109-019-0228-y
Published inApplied Network Science
© The Authors, 2019
The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases. Specifically, we construct semantic networks based on word2vec representation of words, which is “learnt” from large text corpora (Google news, Amazon reviews), and “human built” word networks derived from the well-known lexical databases: WordNet and Moby Thesaurus. We compare “global” (e.g., degrees, distances, clustering coefficients) and “local” (e.g., most central nodes and community-type dense clusters) characteristics of considered networks. Our observations suggest that human built networks possess more intuitive global connectivity patterns, whereas local characteristics (in particular, dense clusters) of the machine built networks provide much richer information on the contextual usage and perceived meanings of words, which reveals interesting structural differences between human built and machine built semantic networks. To our knowledge, this is the first study that uses graph theory and network science in the considered context; therefore, we also provide interesting examples and discuss potential research directions that may motivate further research on the synthesis of lexicographic and machine learning based tools and lead to new insights in this area. ...
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Additional information about fundingThe work of V. Boginski and A. Veremyev was supported in part by the U.S. Air Force Research Laboratory (AFRL) award FA8651-16-2-0009. The work of A. Semenov was supported in part by the U.S. Air Force Research Laboratory (AFRL) European Office of Aerospace Research and Development under Grant FA9550-17-1-0030.
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