Fine-Grained Meetup Events Extraction Through Context-Aware Event Argument Positioning and Recognition

Extracting meetup events from social network posts or webpage announcements is the core technology to build event search services on the Web. While event extraction in English achieves good performance in sentence-level evaluation [1], the quality of auto-labeled training data via distant supervision is not good enough for word-level event extraction due to long event titles [2]. Additionally, meetup event titles are more complex and diverse than trigger-word-based event extraction. Therefore, the performance of event title extraction is usually worse than that of traditional named entity recognition (NER). In this paper, we propose a context-aware meetup event extraction (CAMEE) framework that incorporates a sentence-level event argument positioning model to locate event fields (i.e., title, venue, dates, etc.) within a message and then perform word-level event title, venue, and date extraction. Experimental results show that adding sentence-level event argument positioning as a filtering step improves the word-level event field extraction performance from 0.726 to 0.743 macro-F1, outperforming large language models like GPT-4-turbo (with 0.549 F1) and SOTA NER model SoftLexicon (with 0.733 F1). Furthermore, when evaluating the main event extraction task, the proposed model achieves 0.784 macro-F1.