This paper proposes a deep convolutional neural model for character-based Chinese word segmentation. It first constructs position embeddings to encode unigram and bigram features that are directly related to single positions in input sentence, and then adaptively builds up hierarchical position representations with a deep convolutional net. In addition, a multi-task learning strategy is used to further enhance this deep neural model by treating multiple supervised CWS datasets as different tasks. Experimental results have shown that our neural model outperforms the existing neural ones, and the model equipped with multitask learning has successfully achieved state-of-the-art F-score performance for standard benchmarks: 0.964 on PKU dataset and 0.978 on MSR dataset.
展开▼
