A Probabilistic Soft Logic Based Approach to Exploiting Latent and Global Information in Event Classification

Global information such as event-event association, and latent local information such as fine-grained entity types1, are crucial to event classification. However, existing methods typically focus on sophisticated local features such as part-ofspeech tags, either fully or partially ignoring the aforementioned information. By contrast, this paper focuses on fully employing them for event classification. We notice that it is difficult to encode some global information such as eventevent association for previous methods. To resolve this problem, we propose a feasible approach which encodes global information in the form of logic using Probabilistic Soft Logic model. Experimental results show that, our proposed approach advances state-of-the-art methods, and achieves the best F1 score to date on the ACE data set.

Authors

• Shulin Liu
• Kang Liu
• Shizhu He The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences., Beijing, China
• Jun Zhao The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

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