深层次标签辅助分类任务的层次标签文本分类方法OA北大核心CSTPCD

Hierarchical label text classification is a challenging task in natural language processing,where each document needs to be correctly classified into multiple labels corresponding to a hierarchical structure.However,in the label set,the insufficient semantic information contained in the labels,along with the low number of documents classified into deep labels,inadequate training of deep-level labels leads to significant imbalance problems in label training.A two-channel hierarchical label text classification method with deep label assisted classification task(DLAC)is proposed to deal with the above challenges.The method proposes a deep-level label assisted classifier that effectively uses text features with deep-level labels corresponding to parent label nodes(i.e.,rich features of shallow labels)to improve the classification performance of deep-level labels based on semantic enhancement of labels.Experimental results with eleven algorithms on three datasets demonstrate that the proposed model effectively improves the classification performance of deep-level labels and achieves better results.