CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Distant Supervision for Relation Extraction with Sentence-level Attention and Entity Descriptions
Ji Guoliang; Liu Kang; He Shizhu; Zhao Jun
2017-02
Conference NameAAAI Conference on Artificial Intelligence
Conference Date2017年2月4日至9日
Conference PlaceSan Francisco, California USA
AbstractDistant supervision for relation extraction is an efficient method to scale relation extraction to very large corpora which contains thousands of relations. However, the existing approaches have flaws on selecting valid instances and lack of background knowledge about the entities. In this paper, we propose a sentence-level attention model to select the valid instances, which makes full use of the supervision information from knowledge bases. And we extract entity descriptions from Freebase and Wikipedia pages to supplement background knowledge for our task. The background knowledge not only provides more information for predicting relations, but also brings better entity representations for the attention module. We conduct three experiments on a widely used dataset and the experimental results show that our approach outperforms all the baseline systems significantly.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14517
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorLiu Kang
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ji Guoliang,Liu Kang,He Shizhu,et al. Distant Supervision for Relation Extraction with Sentence-level Attention and Entity Descriptions[C],2017.
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