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We experiment with new methods for learning how related words are positionedrelative to each other in word embedding spaces. Previous approaches learnedconstant vector offsets: vectors that point from source tokens to target tokenswith an assumption that these offsets were parallel to each other. We show thatthe offsets between related tokens are closer to orthogonal than parallel, andthat they have low cosine similarities. We proceed by making a differentassumption; target tokens are linearly separable from source and un-labeledtokens. We show that a max-margin hyperplane can separate target tokens andthat vectors orthogonal to this hyperplane represent the relationship betweensource and targets. We find that this representation of the relationshipobtains the best results in dis-covering linguistic regularities. We experimentwith vector space models trained by a variety of algorithms (Word2vec:CBOW/skip-gram, fastText, or GloVe), and various word context choices such aslinear word-order, syntax dependency grammars, and with and without knowledgeof word position. These experiments show that our model, SVMCos, is robust to arange of experimental choices when training word embeddings.

Discovering linguistic (ir)regularities in word embeddings through max-margin separating hyperplanes