Distantly Supervised Relation Extraction with Layered Attention Mechanism

• 1、School of Cyberspace Security, Beijing University of Posts and Telecommunications , Beijing 100876

Abstract：Distant Supervision has gradually become a main method in Knowladgegraph construction due to it reduced the manpower of annotating data because it uses machines to automatically annotate data. At present, how to better extract sentence features and provide good classification basis for sentence classification (relation extraction) has become a research topic in the field of distant supervision. To solve this problem, this paper uses a network structurecalled the hierarchical attention mechanism, which organizes the attention mechanism into a hierarchical structure to better deal with data noise and capture the characteristics of sentences. In this paper, attention mechanism is used as the main encoder of two levels of features: sentence levle and sentence bag level, and a relational extractor with strong anti-noise ability and distant supervision mechanism is constructed. Experiments show that the model outperforms existing models in capturing sentence features and enhancing generalization capabilities.

Keywords： Deep learning, Natural Language Processing, Knowladge Graph, Relation Extraction, Attention Mechanism, Distant Supervision