【摘 要】
：
Recent studies have shown effectiveness in using neural networks for Chinese Word Segmentation(CWS).However,these models,constrained by the domain and size of the training corpus,do not work well in d
【机 构】
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School of Computer and Information Technology,Beijing Jiaotong University
【出 处】
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第十七届全国计算语言学学术会议暨第六届基于自然标注大数据的自然语言处理国际学术研讨会(CCL 2018)
论文部分内容阅读
Recent studies have shown effectiveness in using neural networks for Chinese Word Segmentation(CWS).However,these models,constrained by the domain and size of the training corpus,do not work well in domain adapta-tion.In this paper,we propose a novel instance-transferring method,which use valuable target domain annotated instances to improve CWS on different do-mains.Specifically,we introduce semantic similarity computation based on character-based n-gram embedding to select instances.Furthermore,training sentences similar to instances are used to help annotate instances.Experimental results show that our method can effectively boost cross-domain segmentation performance.We achieve state-of-the-art results on Internet literatures datasets,and competitive results to the best reported on micro-blog datasets.
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