Research on Architectural Text Classification Method Based on Word Vector Fusion
Due to the complexity and variety of domain-specific terms in architectural questions,the common text classification algorithms are more difficult to classify architectural questions.In order to improve the classification performance of questions in the architectural field,this paper proposes an architectural text classification algorithm based on the fusion of RoBERTa and Word2Vec.Experimental results show that the accuracy rate of the proposed method reaches 91.59％on the construction do-main problem dataset,and the classification performance is better,and on general data sets,the accuracy rate is higher than that of SVM,CNN and other models.
text classificationpretrained language modelsentence vectordeep learningquestion-answering system
万方数据
维普
