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arXiv · 1904.06725

Distributed representation of multi-sense words: A loss-driven approach

Saurav Manchanda·George Karypis
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Abstract

Word2Vec's Skip Gram model is the current state-of-the-art approach for estimating the distributed representation of words. However, it assumes a single vector per word, which is not well-suited for representing words that have multiple senses. This work presents LDMI, a new model for estimating distributional representations of words. LDMI relies on the idea that, if a word carries multiple senses, then having a different representation for each of its senses should lead to a lower loss associated with predicting its co-occurring words, as opposed to the case when a single vector representation is used for all the senses. After identifying the multi-sense words, LDMI clusters the occurrences of these words to assign a sense to each occurrence. Experiments on the contextual word similarity task show that LDMI leads to better performance than competing approaches.

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Saurav Manchanda, George Karypis. 2019-04-14. Distributed representation of multi-sense words: A loss-driven approach. https://doi.org/10.1007/978-3-319-93037-4_27

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