Recently, researchers have demonstrated that both Chinese word and its component characters provide rich semantic information when learning Chinese word embeddings. However, they ignored the semantic similarity across component characters in a word. In this paper, we learn the semantic contribution of characters to a word by exploiting the similarity between a word and its component characters with the semantic knowledge obtained from other languages. We propose a similaritybased method to learn Chinese word and character embeddings jointly. This method is also capable of disambiguating Chinese characters and distinguishing non-compositional Chinese words. Experiments on word similarity and text classification demonstrate the effectiveness of our method.
REF propose a similarity-based characterenhanced word embedding (SCWE) model, exploiting the similarity between a word and its component characters with the semantic knowledge obtained from other languages.
Improve Chinese Word Embeddings by Exploiting Internal Structure
HLT-NAACL
Computer Science