Extracting temporal and causal relations based on event networks |
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| Institution: | 2. Zayed University, United Arab Emirates;1. Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin, China;2. Institute of AI, Shandong Computer Science Center(National Supercomputer Center in Jinan), QILU University of Technology, China;1. Computer Engineering Department, Middle East Technical University, Ankara, Turkey;2. School of Computing Science, University of Glasgow, Glasgow, UK;3. Computer Engineering Department, Bilkent University, Ankara, Turkey;1. The Hong Kong Polytechnic University, Hong Kong, China;2. Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China;3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China;4. Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China;5. Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, China |
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| Abstract: | Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical structure patterns at the sentence-level. In this paper, we address these challenges by proposing an unsupervised event network representation for temporal and causal relation extraction that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal and causal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 and Causal-TimeBank corpora and compare our work with several strong baselines. We show that our method improves performance compared to several strong methods. |
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