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Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities
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Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities
Standard word embeddings lack the possibility to distinguish senses of a word by projecting them to exactly one vector. This has a negative effect particularly when computing similarity scores between words using standard vector-based similarity measures such as cosine similarity. We argue that minor senses play an important role in word similarity computations, hence we use an unsupervised sense inventory resource to retrofit monolingual word embeddings, producing sense-aware embeddings. Using retrofitted sense-aware embeddings, we show improved word similarity and relatedness results on multiple word embeddings and multiple established word similarity tasks, sometimes up to an impressive margin of 0.15 Spearman correlation score.
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