Semi-Supervised Event Extraction Incorporated With Topic Event Frame

Abstract

Supervised Meta-event extraction suffers from two limitations: (1) The extracted meta-events only contain local semantic information and do not present the core content of the text; (2) model performance is easily degraded because of labeled samples with insufficient number and poor quality. To overcome these limitations, this study presents an approach called frame-incorporated semi-supervised topic event extraction (FISTEE), which aims to extract topic events containing global semantic information. Inspired by the frame-based knowledge representation, a topic event frame is developed to integrate multiple meta-events into a topic event. Combined with the tri-training algorithm, a strategy for selecting unlabeled samples is designed to expand the training sets, and labeling models based on conditional random field (CRF) are constructed to label meta-events. The experimental results show that the event extraction performance of FISTEE is better than supervised learning-based approaches. Furthermore, the extracted topic events can present the core content of the text.

A Machine Learning and Large Language Model-Integrated Approach to Research Project Evaluation

“…For topic modelling, large language models can be utilised to identify topics or subject areas within a collection of texts. By applying techniques, such as clustering or latent Dirichlet allocation (LDA), researchers can extract underlying themes or topics from a corpus of qualitative data (Liu & Zuo, 2021;Wu et al, 2023). Large language models can aid in this process by providing topic suggestions or by generating representative examples for each topic (Blei et al, 2003).…”