Word2Sense: Sparse Interpretable Word Embeddings

ACL 2019 Abhishek PanigrahiHarsha Vardhan SimhadriChiranjib Bhattacharyya

We present an unsupervised method to generate Word2Sense word embeddings that are interpretable {---} each dimension of the embedding space corresponds to a fine-grained sense, and the non-negative value of the embedding along the j-th dimension represents the relevance of the j-th sense to the word. The underlying LDA-based generative model can be extended to refine the representation of a polysemous word in a short context, allowing us to use the embedings in contextual tasks... (read more)

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