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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 9

Topic structure enhanced entity coreference resolution in large language models

Liu Xiaoming1a,2,4
Wu Yanbo1a,1b,2
Yang Guan1a,2
Liu Jie3,4
Wu Jiahao1a,1b,2
1. a. School of Artificial Intelligence, b. School of Computer Science, Zhongyuan University of Technology, Zhengzhou Henan 450007, China
2. Zhengzhou Key Laboratory of Text Processing & Image Understanding, Zhengzhou Henan 450007, China
3. School of Information Science, North China University of Technology, Beijing 100144, China
4. Research Center for Language Intelligence of China, Beijing 100089, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0044

Abstract

To address the suboptimal performance of large-scale pre-trained language model (LLM) -based entity coreference resolution (ECR) methods on long texts and the high computational cost associated with full-parameter fine-tuning, this study developed a topic-structure-enhanced ECR model leveraging prompt-based learning. The model utilizes contextual topic structure information to improve the capture of long-range coreference relations. Additionally, a learnable prompt template significantly reduces the computational overhead during fine-tuning. Experimental results demonstrated that the proposed method outperformed baseline models by 2.3%, 0.5%, and 2.6% on three respective public datasets. Furthermore, compared to state-of-the-art models such as Link-Append and Seq2seqCoref, the proposed method achieves approximately 98% of their performance level while using only about 1.1% of the parameters. This demonstrates the model's effectiveness and significant computational efficiency for long-text ECR tasks.

Foundation Support

"新一代人工智能"国家科技重大专项(2020AAA0109703)
国家自然科学基金联合基金重点项目(U23B2029)
国家自然科学基金资助项目(62076167,61772020)
河南省高等学校重点科研项目(24A520058,24A520060,23A520022)
河南省研究生教育改革与质量提升工程项目(YJS2024AL053)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0044
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 9

Publish History

[2025-05-21] Accepted Paper

Cite This Article

刘小明, 吴彦博, 杨关, 等. 主题结构增强的大模型实体共指消解方法 [J]. 计算机应用研究, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0044. (Liu Xiaoming, Wu Yanbo, Yang Guan, et al. Topic structure enhanced entity coreference resolution in large language models [J]. Application Research of Computers, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0044. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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