基于多任务学习的同行评审细粒度情感分析模型  

Fine‑Grained Sentiment Analysis Model of Peer Review Based on Multi‑Task Learning

作　　者：朱金秋 檀健 韩斌彬 殷秀秀 ZHU Jinqiu;TAN Jian;HAN Binbin;YIN Xiuxiu(School of Science,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)

机构地区：[1]南京邮电大学理学院,江苏南京210023

出　　处：《中北大学学报（自然科学版）》2024年第1期105-113,共9页Journal of North University of China(Natural Science Edition)

摘　　要：学术论文同行评审能够直接反映审稿人对论文的主观评价,对审稿文本进行情感分析有利于挖掘审稿人对论文多维度的评价信息。现有的情感分析模型仅能挖掘专家单一的评审维度和相应的情感倾向,本文提出了一种基于多任务学习的同行评审细粒度情感分析模型。该模型在多任务学习框架下,通过在BERT-LCF模型的基础上增加BiLSTM-CRF模块,使其具备了同时完成属性词抽取和细粒度情感分析任务的能力。与传统的基于Pipeline模式的单任务细粒度情感分析模型相比,本模型在保证精度的情况下可以同时完成评审属性提取和情感分析任务。在这两项任务中,所提出模型的F1分数分别达到了89.01%和90.71%。对比实验证明,在多任务场景下,引入BiLSTM-CRF模块对评审文本属性词提取任务有一定的提升作用。The peer review of academic papers can directly reflect the subjective evaluation of reviewers on the papers,and the extraction of sentiment information from the review text is beneficial to mining rich information of reviewers’evaluation on each dimension of the papers.The existing sentiment analysis task could only extract the single review dimension and sentiment of experts.A fine-grained sentiment analysis model for peer review is proposed based on multi-task learning.The model is equipped with the ability to accomplish both attribute word extraction and fine-grained sentiment analysis tasks by adding the BiLSTM-CRF module to the BERT-LCF model in a multi-task learning framework.Compared with the traditional single-task fine-grained sentiment analysis model based on the Pipeline model,the proposed model can complete the review attribute extraction and sentiment analysis tasks simultaneously while ensur‐ing the accuracy of the model.In the two tasks,F1-score of the proposed model reaches 89.01%and 90.71%,respectively.In addition,the introduction of BiLSTM-CRF module has a certain enhancement effect on the review text attribute word extraction task in a multi-task scenario,as demonstrated by com‐parison experiments.

关 键 词：同行评审 多任务学习 属性词抽取 细粒度情感分析 BiLSTM-CRF 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]