This paper hypothesizes that better word embeddings can be learned by representing words and subwords by different lengths of vectors. To investigate the impact of the length of subword vectors on word embeddings, this paper proposes a model based on the Subword Information Skip-gram model. 'The experiments on two datasets with respect to two tasks show that the proposed model outperforms 6 baselines, which confirms the aforementioned hypothesis. In addition, we also observe that, within a specific range, a higher dimensionality of subword vectors always improve the quality of word embeddings.
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