CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation
Wang CQ(汪春奇)1,2; Xu B(徐波)1
2017
Conference Name第八届国际自然语言处理研讨会
Pages163-172
Conference Date2017年11月
Conference Place台北
Abstract

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.

KeywordChinese Word Segmentation Convolutional Neural Network Word Embedding
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19674
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation1.中科院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang CQ,Xu B. Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation[C],2017:163-172.
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