Abstract
Event relation identification aims to identify relations between events in texts, including causal relation, temporal relation, sub-class relation and so on. Most of the research focuses on temporal relation and causal relation. Extracting events and the relation between events is an essential step to build an event-centric knowledge graph, which plays an important role in story ending prediction and decision-making. The form of causal and temporal relation in natural language text is diverse, sparse and complex which brings challenges to relation identification. In recent years, the integration of deep learning and knowledge has promoted the relation identification progress. This paper describes in detail the characteristics of causal and temporal relations in natural language texts and their connections. What is more, this paper surveys existing approaches based on pattern matching, machine learning and deep learning. Besides, this paper analyzes corpus and points out the future development direction and contributes ideas to further improve relation identification between events. To our knowledge, this is the first paper to survey the event relation identification.
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Liu, Y., Tian, J., Zhang, L., Feng, Y., Fang, H. (2021). A Survey on Event Relation Identification. In: Chen, H., Liu, K., Sun, Y., Wang, S., Hou, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. CCKS 2020. Communications in Computer and Information Science, vol 1356. Springer, Singapore. https://doi.org/10.1007/978-981-16-1964-9_14
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