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	Reasoning Over Relations Based on Chinese Knowledge Bases
	Ji, Guoliang; Zhang, Yinghua; Hao, Hongwei; Zhao, Jun
	2014-10
会议日期	2014年10月18日至19日
会议地点	中国湖北武汉
英文摘要	Knowledge bases are useful resource for many applications, but reasoning new relationships between new entities based on them is difficult because they often lack the knowledge of new relations and entities. In this paper, we introduce the novel Neural Tensor Network (NTN)[1] model to reason new facts based on Chinese knowledge bases. We represent entities as an average of their constituting word or character vectors, which share the statistical strength between entities, such as 荔枝巢蛾 and 荔枝异形小卷蛾. The NTN model uses a tensor network to replace a standard neural layer, which strengthen the interaction of two entity vectors in a simple and efficient way. In experiments, we compare the NTN and several other models, the results show that all models’ performance can be improved when word vectors are pre-trained from an unsupervised large corpora and character vectors don’t have this advantage. The NTN model outperforms others and reachs high classification accuracy 91.1% and 89.6% when using pre-trained word vectors and random character vectors, respectively. Therefore, when Chinese word segmentation is a difficult task, initialization with random character vectors is a feasible choice.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/14519]
专题	自动化研究所_模式识别国家重点实验室_自然语言处理团队
作者单位	Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Ji, Guoliang,Zhang, Yinghua,Hao, Hongwei,et al. Reasoning Over Relations Based on Chinese Knowledge Bases[C]. 见:. 中国湖北武汉. 2014年10月18日至19日.

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