Summary of A Survey Of Event Causality Identification: Principles, Taxonomy, Challenges, and Assessment, by Qing Cheng et al.
A Survey of Event Causality Identification: Principles, Taxonomy, Challenges, and Assessment
by Qing Cheng, Zefan Zeng, Xingchen Hu, Yuehang Si, Zhong Liu
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey systematically addresses the foundational principles, technical frameworks, and challenges of Event Causality Identification (ECI) in Natural Language Processing (NLP). It offers a comprehensive taxonomy to categorize and clarify current research methodologies and a quantitative assessment of existing models. The paper establishes a conceptual framework for ECI, outlining key definitions, problem formulations, and evaluation standards. The taxonomy classifies ECI methods according to the two primary tasks of sentence-level (SECI) and document-level (DECI) event causality identification. The survey examines various approaches, including feature pattern-based matching, deep semantic encoding, causal knowledge pre-training and prompt-based fine-tuning, external knowledge enhancement methods for SECI, and event graph reasoning and prompt-based techniques for DECI. The paper also analyzes the strengths, limitations, and open challenges of each approach and conducts an extensive quantitative evaluation on two benchmark datasets. Finally, it explores future research directions to overcome current limitations and broaden ECI applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Event Causality Identification (ECI) is a crucial task in Natural Language Processing (NLP). Researchers have been working hard to develop new methods for automatically extracting causalities from textual data. This survey brings together all the important work that has been done so far, providing a comprehensive overview of the field. It explains what ECI is and why it’s important, and then looks at different approaches people have taken to solve this problem. The paper also compares these approaches, highlighting their strengths and weaknesses. By understanding what works and what doesn’t, we can start to develop even better methods for ECI. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Nlp » Prompt
