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	Distant Supervision for Relation Extraction with Sentence-level Attention and Entity Descriptions
	Ji Guoliang; Liu Kang; He Shizhu; Zhao Jun
	2017-02
会议日期	2017年2月4日至9日
会议地点	San Francisco, California USA
英文摘要	Distant 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.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/41026]
专题	模式识别国家重点实验室_自然语言处理
通讯作者	Liu Kang
推荐引用方式 GB/T 7714	Ji Guoliang,Liu Kang,He Shizhu,et al. Distant Supervision for Relation Extraction with Sentence-level Attention and Entity Descriptions[C]. 见:. San Francisco, California USA. 2017年2月4日至9日.

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