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A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper.First,the proposed Chinese character learning model uses the semantics of local context and global context to learn the representation of Chinese characters.Then,Chinese word segmentation model is built by a neural network,while the segmentation model is trained with the character representations as its input features.Finally,experimental results show that Chinese character representations can effectively learn the semantic information.Characters with similar semantics cluster together in the visualize space.Moreover,the proposed Chinese word segmentation model also achieves a pretty good improvement on precision,recall and f-measure.
A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representationations is proposed in this paper. First, the proposed Chinese character learning model uses the semantics of local context and global context to learn the representation of Chinese characters.Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the character representation as its input features. Finally, experimental results show that Chinese character representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.