Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (2): 60-72.doi: 10.3969/j.issn.1000-5641.2023.02.008

• Computer Science • Previous Articles     Next Articles

Target-dependent event detection from news

Tiantian ZHANG, Man LAN*()   

  1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
  • Received:2021-09-28 Online:2023-03-25 Published:2023-03-23
  • Contact: Man LAN


In real-world scenarios, various events in the news are not only too nuanced and complex to distinguish, but also involve multiple entities. To address these problems, previous event-centric methods are designed to detect events first and then extract arguments, relying on imperfect performance for event trigger detection; this process, however, is unfit to deal with the sheer volume of news in the real world. Given that the performance of named entity recognition (NER) is satisfactory, we shift our perspective from an event-centric to a target-centric view. This paper proposes a new task: target-dependent event detection (TDED), which aims to extract target entities and detect their corresponding events. We also propose a semantic and syntactic aware approach to support thousands of target entity extractions first and subsequently the detection of dozens of event types; this approach can be applied to data from massive corporations. Experimental results on a real-world Chinese financial dataset demonstrated that our model outperformed previous methods, particularly in complex scenarios.

Key words: target-dependent, event detection, entity recognition, event keywords, syntactic dependency distance

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