While previous studies show that modeling the minimum meaning-bearing units (characters or morphemes) benefits learning vector representations of words, they ignore the semantic dependencies across these units when deriving word vectors. In this work, we propose to improve the learning of Chinese word embeddings by exploiting semantic knowledge. The basic idea is to take the semantic knowledge about words and their component characters into account when designing composition functions. Experiments show that our approach outperforms two strong baselines on word similarity, word analogy, and document classification tasks.
CITATION STYLE
Yang, L., & Sun, M. (2015). Improved learning of chinese word embeddings with semantic knowledge. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9427, pp. 15–25). Springer Verlag. https://doi.org/10.1007/978-3-319-25816-4_2
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