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There have been some works that learn a lexicon together with the corpus toimprove the word embeddings. However, they either model the lexicon separatelybut update the neural networks for both the corpus and the lexicon by the samelikelihood, or minimize the distance between all of the synonym pairs in thelexicon. Such methods do not consider the relatedness and difference of thecorpus and the lexicon, and may not be the best optimized. In this paper, wepropose a novel method that considers the relatedness and difference of thecorpus and the lexicon. It trains word embeddings by learning the corpus topredicate a word and its corresponding synonym under the context at the sametime. For polysemous words, we use a word sense disambiguation filter toeliminate the synonyms that have different meanings for the context. Toevaluate the proposed method, we compare the performance of the word embeddingstrained by our proposed model, the control groups without the filter or thelexicon, and the prior works in the word similarity tasks and textclassification task. The experimental results show that the proposed modelprovides better embeddings for polysemous words and improves the performancefor text classification.

Improve Lexicon-based Word Embeddings By Word Sense Disambiguation