Work

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

Year: 2024

Type: article

Abstract: 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 WKGS19,the quality of auto-labeled training data via distant supervision is not good enough for word-level event extraction due to long event titles JISE2022.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.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). If we focus on the main event, the proposed model achieves 0.784 macro-F1.

Source: International Journal of Computational Intelligence Systems

DOI: 10.1007/s44196-024-00697-0

Authors: Yuan-Hao Lin, Chia-Hui Chang, Hsiu-Min Chuang

Institutions: National Central University, Chung Yuan Christian University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,290

Funders: National Science and Technology Council, National Science and Technology Council