Summary of Document-level Event Extraction with Definition-driven Icl, by Zhuoyuan Liu et al.
Document-Level Event Extraction with Definition-Driven ICL
by Zhuoyuan Liu, Yilin Luo
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have demonstrated promise in document-level event extraction tasks within Natural Language Processing (NLP). However, existing methods face challenges in designing effective prompts. To address this issue, we propose “Definition-driven Document-level Event Extraction (DDEE),” an optimization strategy that adjusts prompt length and clarifies heuristics to improve event extraction performance. We employed data balancing techniques to solve the long-tail effect problem, enhancing generalization for event types. Our refined prompts ensured concision while adapting to LLM sensitivity to style. Structured heuristic methods and strict limiting conditions further improved precision in event and argument role extraction. DDEE not only solves prompt engineering problems but also paves the way for event extraction technology development and new research perspectives in NLP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using computers to automatically find important events in documents. This is called “event extraction” and it’s a big deal in Natural Language Processing (NLP). Right now, computers are not very good at this because they don’t know how to ask the right questions. Our solution is called DDEE (Definition-driven Document-level Event Extraction) and it helps computers ask better questions by adjusting the way we ask them. We also made sure that our questions are clear and concise, which makes a big difference. This new approach not only improves event extraction but also opens up new possibilities for using computers to understand language. |
Keywords
» Artificial intelligence » Generalization » Natural language processing » Nlp » Optimization » Precision » Prompt
