| Title | A Hierarchical Fine-Tuning Based Approach for Multi-Label Text Classification |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Zhang, Qiang, Chai, Bo, Song, Bochuan, Zhao, Jingpeng |
| Conference Name | 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) |
| Keywords | composability, Computational modeling, hierarchical fine-tuning, Human Behavior, human factors, Logic gates, Mathematical model, Metrics, Neurons, ordered neurons LSTM, pubcrawl, Scalability, Taxonomy, text analytics, text categorization, text classification, text embedding, Training |
| Abstract | 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. |
| DOI | 10.1109/ICCCBDA49378.2020.9095668 |
| Citation Key | zhang_hierarchical_2020 |
A Hierarchical Fine-Tuning Based Approach for Multi-Label Text Classification