摘要
【目的】提出AttentionSBGMC深度学习模型,以提升引文情感和引文目的分类的性能。【方法】采用SciBERT预训练模型得到语料集中句子的语义表示向量,根据文本特点,依次通过BiGRU神经网络和多尺度卷积神经网络(Multi-CNN)提取句子中的时序全局特征和局部关键特征,引入注意力机制对提取出的特征重新分配权重,达到突出关键特征的目的,最后通过线性层实现引文情感和引文目的自动分类。【结果】在Abu-Jbara数据集上,引文情感主客观、引文情感正负面、引文目的三项分类任务的F1值分别为86.74%、91.14%和84.92%;在Athar数据集上,引文情感主客观、引文情感正负面两项分类任务的F1值分别为88.50%和86.59%。【局限】鉴于公开的引文数据集的有限性,该模型仅在两个英文数据集上进行验证,在其他数据集上的泛化性能有待进一步验证。【结论】所提AttentionSBGMC深度学习模型能全面、有效地提取出语料文本中的重要特征,可以更为准确地实现引文情感和引文目的自动分类。
[Objective]This paper proposes a deep learning model——AttentionSBGMC to improve the automatic classification of citation sentiment and purposes.[Methods]First,we used the SciBERT pre-training model to obtain the semantic representation vector for the sentences.Then,according to the characteristics of the texts,we used the BiGRU neural network and the multi-scale convolutional neural network(Multi-CNN)to extract their temporal global features and local key features.Third,we utilized the attention model to highlight the key features by redistributing the extracted features’weights.Finally,we finished the classification tasks with the help of linear layers.[Results]We examined the new method with two citation data sets.With Abu-Jbara data set the F1 values in three classification tasks(for subjective and objective citation emotion,positive and negative citation emotion,and citation purpose)were 86.74%,91.14%and 84.92%,respectively.With Athar data set the F1 values in two classification tasks(for subjective and objective citation emotion,positive and negative citation emotion)were 88.50%,86.59%,respectively.[Limitations]The proposed model was only examined on English data sets,which needs to be expanded in the future.[Conclusions]The proposed model could effectively extract the important corpus features,and automatically classify citation sentiment and purposes.
作者
周文远
王名扬
井钰
Zhou Wenyuan;Wang Mingyang;Jing Yu(College of Information and Computer Engineering,Northeast Forestry University,Harbin 150040,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2021年第12期48-59,共12页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金项目(项目编号:71473034)的研究成果之一。
