Year: 2025
Type: article
Abstract: Achieving high accuracy in Chinese event prediction remains challenging due to the prevalence of short-text events that lack sufficient background knowledge and semantic depth. Existing approaches predominantly rely on knowledge augmentation strategies that adopt a retrieval-and-integration paradigm, which often results in suboptimal performance due to the limited scope and timeliness of KGs (Knowledge Graphs). Large language models (LLMs) play a crucial role in event knowledge augmentation by serving as dynamic, context-aware knowledge generators that address the limitations of KGs. This paper introduces an enhanced method for Chinese event prediction, named CEP-PKA (Chinese Event Prediction with Prompt-driven Knowledge Augmentation). Specifically, we design a prompt engineering strategy to leverage an LLM to generate fine-grained knowledge for event elements, including triggers and arguments. We then fine-tune a pre-trained language model (PLM) to learn a joint representation of events and its associated knowledge. Experimental results in two public Chinese benchmarks demonstrate that CEP-PKA outperforms baseline models relying solely on LLMs or PLMs, achieving average accuracy improvements of 10.62% and 12.51%, respectively.
Source: Applied Sciences
DOI: 10.3390/app152312543
Authors: Shulan Lou, Xiaoxue Xie, Wei Liu, Wangtao Jiang
Institution: Shanghai University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G8645408799
OpenAlex ID: W4416718786