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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
会议名称AAAI Conference on Artificial Intelligence
会议日期2017年2月4日至9日
会议地点San Francisco, California USA
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.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14517
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
作者单位Institute of Automation, Chinese Academy of Sciences
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],2017.
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