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
Conference NameThe China National Conference on Computational Linguistics
Conference Date2014年10月18日至19日
Conference Place中国湖北武汉
AbstractKnowledge 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.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14519
Collection模式识别国家重点实验室_自然语言处理
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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