Abstract
Distantly-supervised relation extraction has proven to be effective to find relational facts from texts. However, the existing approaches treat the instances in the same bag independently and ignore the semantic structural information. In this paper, we propose a graph convolution network (GCN) model with an attention mechanism to improve relation extraction. For each bag, the model first builds a graph through the dependency tree of each instance in this bag. In this way, the correlations between instances are built through their common words. The learned node (word) embeddings which encode the bag information are then fed into the sentence encoder, i.e., text CNN to obtain better representations of sentences. Besides, an instance-level attention mechanism is introduced to select valid instances and learn the textual relation embedding. Finally, the learned embedding is used to train our relation classifier. Experiments on two benchmark datasets demonstrate that our model significantly outperforms the compared baselines.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61772082, 61806020, 61702296, 61972047), the National Key Research and Development Program of China (2017YFB0803304), the Beijing Municipal Natural Science Foundation (4182043), the CCF-Tencent Open Fund, and the Fundamental Research Funds for the Central Universities.
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Zhang, L., Hu, L., Shi, C. (2020). Incorporating Instance Correlations in Distantly Supervised Relation Extraction. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Lecture Notes in Computer Science(), vol 12032. Springer, Cham. https://doi.org/10.1007/978-3-030-41407-8_12
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