A Hierarchical Multi-Label Bidding Section Classification Method Based on K-BERT-LDA
Traditional manual bidding has low efficiency and accuracy in dividing bids.A hierarchical multi label text classification method based on K-BERT-LDA is proposed for material bidding texts with sparse semantic features and obvious hierarchical structure of labels.First-ly,text features are extracted through a hybrid model.The K-BERT model extracts text features with knowledge injection to compensate for se-mantic information gaps.The LDA topic model extracts topic distribution features and further enriches the text feature representation through feature fusion.Secondly,joint embedding of category labels,where the prediction results of upper level labels can guide lower level classifica-tion and fully utilize the tree structure relationship between labels to improve the accuracy of multi label text classification.Finally,an intelli-gent processing strategy based on text similarity algorithm is proposed to ensure the success rate of bidding and obtain bidding results by merg-ing sections with insufficient pre investment amounts.The experiment shows that the proposed method has better classification performance than other classification methods and single model,and the accuracy,precision and F1 value reach 95.45%,92.57%and 91.88%respectively,which can effectively and accurately achieve the goal of intelligent classification.
bidding divisionhierarchical multi-label text classificationknowledge injectiontopic distributionfeature fusiontext simi-larity
国家科技期刊平台
NSTL
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