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Distributed word representations are used in many natural language processing tasks. When dealing with ambiguous words, it is desired to generate multi-sense embeddings, i.e., multiple representations... per word. Therefore, several methods have been proposed to generate different word representations based on parts of speech or topic, but these methods tend to be too coarse to deal with ambiguity. In this paper, we propose methods to generate multiple word representations for each word based on dependency structure relations. In order to deal with the data sparseness problem due to the increase in the size of vocabulary, the initial value for each word representations is determined using pre-trained word representations. It is expected that the representations of low frequency words will remain in the vicinity of the initial value, which will in turn reduce the negative effects of data sparseness. Extensive evaluation results confirm the effectiveness of our methods that significantly outperformed state-of-the-art methods for multi-sense embeddings. Detailed analysis of our method shows that the data sparseness problem is resolved due to the pre-training.続きを見る
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