中国科学院自动化研究所机构知识库

Character-based sequence labeling frame- work is flexible and efficient for Chi- nese word segmentation (CWS). Recently, many character-based neural models have been applied to CWS. While they obtain good performance, they have two obvious weaknesses. The first is that they heav- ily rely on manually designed bigram fea- ture, i.e. they are not good at captur- ing n-gram features automatically. The second is that they make no use of full word information. For the first weakness, we propose a convolutional neural model, which is able to capture rich n-gram fea- tures without any feature engineering. For the second one, we propose an effective approach to integrate the proposed model with word embeddings. We evaluate the model on two benchmark datasets: PKU and MSR. Without any feature engineer- ing, the model obtains competitive per- formance — 95.7% on PKU and 97.3% on MSR. Armed with word embeddings, the model achieves state-of-the-art perfor- mance on both datasets — 96.5% on PKU and 98.0% on MSR, without using any ex- ternal labeled resource.