New Word Pair Level Embeddings to Improve Word Pair Similarity

Abstract

We present a novel approach for computing similarity of English word pairs. While many previous approaches compute cosine similarity of individually computed word embeddings, we compute a single embedding for the word pair that is suited for similarity computation. Such embeddings are then used to train a machine learning model. Testing results on MEN and WordSim-353 datasets demonstrate that for the task of word pair similarity, computing word pair embeddings is better than computing word embeddings only.