Yankai Lin1 , Shiqi Shen1 , Zhiyuan Liu1,2∗ , Huanbo Luan1 , Maosong Sun1,2 1 Department of Computer Science and Technology, State Key Lab on Intelligent Technology and Systems, National Lab for Information Science and Technology, Tsinghua University, Beijing, China 2 Jiangsu Collaborative Innovation Center for Language Competence, Jiangsu, China
Distant supervised relation extraction has been widely used to find novel relational facts from text. However, distant supervision inevitably accompanies with the wrong labelling problem, and these noisy data will substantially hurt the performance of relation extraction. To alleviate this issue, we propose a sentence-level attention-based model for relation extraction. In this model, we employ convolutional neural networks to embed the semantics of sentences. Afterwards, we build sentence-level attention over multiple instances, which is expected to dynamically reduce the weights of those noisy instances. Experimental results on real-world datasets show that, our model can make full use of all informative sentences and effectively reduce the influence of wrong labelled instances. Our model achieves significant and consistent improvements on relation extraction as compared with baselines. The source code of this paper can be obtained from https: //github.com/thunlp/NRE.
In recent years, various large-scale knowledge bases (KBs) such as Freebase (Bollacker et al., 2008), DBpedia (Auer et al., 2007) and YAGO (Suchanek et al., 2007) have been built and widely used in many natural language processing (NLP) tasks, including web search and question answering. These KBs mostly compose of relational facts with triple format, e.g., (Microsoft, founder, Bill Gates). Although existing KBs contain a ∗
Corresponding author: Zhiyuan Liu (firstname.lastname@example.org).
massive amount of facts, they are still far from complete compared to the infinite real-world facts. To enrich KBs, many efforts have been invested in automatically finding unknown relational facts. Therefore, relation extraction (RE), the process of generating relational data from plain text, is a crucial task in NLP.