Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this work, we innovatively develop two component-enhanced Chinese character embedding models and their bigram extensions. Distinguished from English word embeddings, our models explore the compositions of Chinese characters, which often serve as semantic indictors inherently. The evaluations on both word similarity and text classification demonstrate the effectiveness of our models.
CITATION STYLE
Li, Y., Li, W., Sun, F., & Li, S. (2015). Component-enhanced Chinese character embeddings. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing (pp. 829–834). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d15-1098
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