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- Title
Hierarchical Label Text Classification Method with Deep Label Assisted Classification Task.
- Authors
CAO Yukun; WEI Ziyue; TANG Yijia; JIN Chengkun; LI Yunfeng
- Abstract
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.
- Subjects
NATURAL language processing; HIERARCHICAL Bayes model; NAIVE Bayes classification; CLASSIFICATION
- Publication
Journal of Computer Engineering & Applications, 2024, Vol 60, Issue 10, p105
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2302-0237