Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction
This addresses the problem of scaling event extraction to massive event types for NLP researchers and practitioners, representing a significant advancement rather than an incremental improvement.
The paper tackles the challenge of extracting events with massive types by proposing a collaborative LLM-based annotation method that creates the largest EE dataset to date (EEMT with 200,000+ samples, 3,465 event types) and an LLM-based partitioning extraction method (LLM-PEE) that outperforms SOTA by 5.4-6.1 points in supervised settings and achieves up to 12.9 improvement in zero-shot settings.
Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from two aspects: 1) The absence of an efficient and effective annotation method. 2) The absence of a powerful extraction method can handle massive types. For the first challenge, we propose a collaborative annotation method based on Large Language Models (LLMs). Through collaboration among multiple LLMs, it first refines annotations of trigger words from distant supervision and then carries out argument annotation. Next, a voting phase consolidates the annotation preferences across different LLMs. Finally, we create the EEMT dataset, the largest EE dataset to date, featuring over 200,000 samples, 3,465 event types, and 6,297 role types. For the second challenge, we propose an LLM-based Partitioning EE method called LLM-PEE. To overcome the limited context length of LLMs, LLM-PEE first recalls candidate event types and then splits them into multiple partitions for LLMs to extract events. The results in the supervised setting show that LLM-PEE outperforms the state-of-the-art methods by 5.4 in event detection and 6.1 in argument extraction. In the zero-shot setting, LLM-PEE achieves up to 12.9 improvement compared to mainstream LLMs, demonstrating its strong generalization capabilities.