CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Integrating Surface and Abstract Features for Robust Cross-Domain Chinese Word Segmentation
Li XQ(李小青); Wang Kun; Zong Chengqing; Su Keh-Yih; Li, Xiaoqing
2012
会议名称International Conference on Computational Linguistics
会议录名称Proceedings of 24th International Conference on Computational Linguistics
会议日期8-15, December
会议地点Bombay, India
摘要
Current character-based approaches are not robust for cross domain Chinese word segmentation. In this paper, we alleviate this problem by deriving a novel enhanced character-based generative model with a new abstract aggregate candidate-feature, which indicates if the given candidate prefers the corresponding position-tag of the longest dictionary matching word. Since the distribution of the proposed feature is invariant across domains, our model thus possesses better generalization ability. Open tests on CIPS-SIGHAN-2010 show that the enhanced generative model achieves robust cross-domain performance for various OOV coverage rates and obtains the best performance on three out of four domains. The enhanced generative model is then further integrated with a discriminative model which also utilizes dictionary information. This integrated model is shown to be either superior or comparable to all other models reported in the literature
on every domain of this task.
关键词Word Segmentation Dictionary
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13017
专题模式识别国家重点实验室_自然语言处理
通讯作者Li, Xiaoqing
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Li XQ,Wang Kun,Zong Chengqing,et al. Integrating Surface and Abstract Features for Robust Cross-Domain Chinese Word Segmentation[C],2012.
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