A Hierarchical Fine-Tuning Based Approach for Multi-label Text Classification
2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)(2020)
摘要
Hierarchical Text classification has recently become increasingly challenging with the growing number of classification labels. In this paper, we propose a hierarchical fine-tuning based approach for hierarchical text classification. We use the ordered neurons LSTM (ONLSTM) model by combining the embedding of text and parent category for hierarchical text classification with a large number of categories, which makes full use of the connection between the upper-level and lower-level labels. Extensive experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.
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关键词
text classification,hierarchical fine-tuning,ordered neurons LSTM,text embedding
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