Semantic piecewise convolutional neural network with adaptive negative training for distantly supervised relation extraction

Abstract

Distantly Supervised Relation Extraction (DSRE) aligns existing knowledge bases with unstructured text to extract relation facts, and its automatically generated training data is inevitably noisy. Most existing works identify and reduce the impact of noise by enhancing semantic features. However, they only consider the semantic information in a single instance and ignore the semantic information between different instances. In this work, we propose a Semantic Piecewise Convolutional Neural Network (SPCNN), which uses the similarity between different entity pairs as semantic information to improve relation extraction. Specifically, to learn better semantic vector representations, we combine position features with entity pair features and entity similarity features in a high-dimensional space respectively, and generating two different semantic-aware representations. Then we unify these two representations to form a high-quality bag representation for training. Moreover, we design an Adaptive Negative Training (ANT) strategy, which facilitates the network to further exploit the rich semantic features to reduce the interference of noisy labels. Extensive experimental results on a large-scale benchmark dataset show that our method significantly outperforms other baselines.