Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs
Traditional word vectors, such as word2vec and glove, have a well known inclination to conflate the semantic similarity with other semantic relations. A retrofitting procedure may be needed to solve this issue. In this work, we propose a new retrofitting method called Heterogeneously Retrofitted Spectral Word Embedding. It heterogeneously twists the similarity matrix of word pairs with lexical constraints. A new set of word vectors is generated by a spectral decomposition of the similarity matrix, which has a linear algebraic analytic form. Our method has a competitive performance compared with the state of the art retrofitting method such as AR (CITATION) In addition, since our embedding has a clear linear algebraic relationship with the similarity matrix, we carefully study the contribution of each component in our model. Last but not least, our method is very efficient to execute.
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