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Joint Learning of Sense and Word Embeddings

Methods for learning lower-dimensional representations (embeddings) of words using unlabelled data have received a renewed interested due to their myriad success in various Natural Language Processing (NLP) tasks. However, despite their success, a common deficiency associated with most word embedding learning methods is that they learn a single representation for a word, ignoring the different senses of that word (polysemy). To address the polysemy problem, we propose a method that jointly learns sense-aware word embeddings using both unlabelled and sense-tagged text corpora. In particular, our proposed method can learn both word and sense embeddings by efficiently exploiting both types of resources. Our quantitative and qualitative experimental results using unlabelled text corpus with (a) manually annotated word senses, and (b) pseudo annotated senses demonstrate that the proposed method can correctly learn the multiple senses of an ambiguous word. Moreover, the word embeddings learnt by our proposed method outperform several previously proposed competitive word embedding learning methods on word similarity and short-text classification benchmark datasets.