Joint Embedding of Words and Category Labels for Hierarchical Multi-label Text Classification
This work addresses the challenge of handling large-scale and fine-grained label hierarchies in text classification, which is an incremental improvement for applications requiring efficient hierarchical categorization.
The paper tackles hierarchical multi-label text classification by proposing a joint embedding of text and parent category labels using a hierarchical fine-tuning ordered neurons LSTM (HFT-ONLSTM), which improves performance over state-of-the-art hierarchical models at lower computational cost.
Text classification has become increasingly challenging due to the continuous refinement of classification label granularity and the expansion of classification label scale. To address that, some research has been applied onto strategies that exploit the hierarchical structure in problems with a large number of categories. At present, hierarchical text classification (HTC) has received extensive attention and has broad application prospects. Making full use of the relationship between parent category and child category in text classification task can greatly improve the performance of classification. In this paper, We propose a joint embedding of text and parent category based on hierarchical fine-tuning ordered neurons LSTM (HFT-ONLSTM) for HTC. Our method makes full use of the connection between the upper-level and lower-level labels. Experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.