CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Reasoning Over Relations Based on Chinese Knowledge Bases
Ji, Guoliang; Zhang, Yinghua; Hao, Hongwei; Zhao, Jun
2014-10
会议名称The China National Conference on Computational Linguistics
会议日期2014年10月18日至19日
会议地点中国湖北武汉
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.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14519
专题模式识别国家重点实验室_自然语言处理
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],2014.
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