Large Scaled Relation Extraction with Reinforcement Learning | |
Zeng Xiangrong1,2; Liu Kang1,2; He Shizhu1,2; Zhao Jun1,2 | |
2018 | |
会议日期 | 2018 |
会议地点 | 美国 |
英文摘要 | Sentence relation extraction aims to extract relational facts from sentences, which is an important task in natural language processing field. Previous models rely on the manually labeled supervised dataset. However, the human annotation is costly and limits to the number of relation and data size, which is difficult to scale to large domains. In order to conduct largely scaled relation extraction, we utilize an existing knowledge base to heuristically align with texts, which not rely on human annotation and easy to scale. However, using distant supervised data for relation extraction is facing a new challenge: sentences in the distant supervised dataset are not directly labeled and not all sentences that mentioned an entity pair can represent the relation between them. To solve this problem, we propose a novel model with reinforcement learning. The relation of the entity pair is used as distant supervision and guide the training of relation extractor with the help of reinforcement learning method. We conduct two types of experiments on a publicly released dataset. Experiment results demonstrate the effectiveness of the proposed method compared with baseline models, which achieves 13.36\% improvement. |
产权排序 | 1 |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/20626] |
专题 | 自动化研究所_模式识别国家重点实验室_自然语言处理团队 自然语言处理团队 |
通讯作者 | Liu Kang |
作者单位 | 1.中国科学院大学 2.中科院自动化所 |
推荐引用方式 GB/T 7714 | Zeng Xiangrong,Liu Kang,He Shizhu,et al. Large Scaled Relation Extraction with Reinforcement Learning[C]. 见:. 美国. 2018. |
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