A storytree-based model for inter-document causal relation extraction from news articles

With more and more news articles appearing on the Internet, discovering causal relations between news articles is very important for people to understand the development of news. Extracting the causal relations between news articles is an inter-document relation extraction task. Existing works on relation extraction cannot solve it well because of the following two reasons: (1) most relation extraction models are intra-document models, which focus on relation extraction between entities. However, news articles are many times longer and more complex than entities, which makes the inter-document relation extraction task harder than intra-document. (2) Existing inter-document relation extraction models rely on similarity information between news articles, which could limit the performance of extraction methods. In this paper, we propose an inter-document model based on storytree information to extract causal relations between news articles. We adopt storytree information to integer linear programming (ILP) and design the storytree constraints for the ILP objective function. Experimental results show that all the constraints are effective and the proposed method outperforms widely used machine learning models and a state-of-the-art deep learning model, with F1 improved by more than 5% on three different datasets. Further analysis shows that five constraints in our model improve the results to varying degrees and the effects on the three datasets are different. The experiment about link features also suggests the positive influence of link information.