软件导刊2026，Vol.25Issue(4)：123-131,9.DOI:10.11907/rjdk.251554
基于预训练模型的专家匹配任务分类重构
Classification-based Reframing of Expert Matching Tasks Powered by Pretrained Models
摘要
Abstract
In research project evaluation scenarios requiring"small-circle peer review,"traditional expert retrieval/matching methods rely on semantic alignment between project descriptions and expert resumes(dual-text),facing practical challenges such as scarcity of expert resume data and semantic drift of cross-disciplinary terms.To address these issues,this paper proposes a classification-based reframing model for matching tasks using pretrained models.Based on paper text-expert label data pairs,the model trains a neural network that maps paper text to expert labels,transforming the project-expert matching task into a"text-to-label"classification problem.The model requires only the project description as input and directly outputs expert identity labels,thereby bypassing the dependency on resumes and reducing the difficulty of term alignment.To mitigate the scarcity of annotated Chinese expert data,sentiment classification is introduced as a proxy task to systematical-ly evaluate the performance differences among five models:BERT-base-Chinese,CNN,LSTM,RNN,and SVM/TF-IDF.Experiments show that BERT-base-Chinese achieves an accuracy rate above 97%and an F1_weighted-score above 97%on a self-constructed cross-disciplin-ary dataset,significantly outperforming other models.Its Whole Word Masking(WWM)technique and layered fine-tuning strategy effectively resolve term semantic drift.关键词
专家匹配/预训练模型/文本分类/跨域术语消歧Key words
expert matching/pretrained models/text classification/cross-domain term disambiguation分类
信息技术与安全科学引用本文复制引用
邓家亮,祝家东,邵长城,陈国梁,陈平华..基于预训练模型的专家匹配任务分类重构[J].软件导刊,2026,25(4):123-131,9.基金项目
广东省软科学研究计划项目(2025A1010010002) （2025A1010010002）
佛山市密码工程与可信系统重点实验室项目(FS2025016) （FS2025016）
广东省普通高校重点科研项目(2024KCXTD090) （2024KCXTD090）
佛山职业技术学院校级科研项目(KY2025Z12) （KY2025Z12）
