HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification
This work addresses the challenging problem of accurately classifying texts into large sets of closely related hierarchical categories, which is incremental as it builds on existing hierarchical and flat classification approaches.
The paper tackles hierarchical multi-label text classification by proposing HFT-ONLSTM, which uses joint embeddings and fine-tuning to improve accuracy level-by-level, achieving superior performance and reduced computational costs compared to state-of-the-art methods on two benchmark datasets.
Many important classification problems in the real-world consist of a large number of closely related categories in a hierarchical structure or taxonomy. Hierarchical multi-label text classification (HMTC) with higher accuracy over large sets of closely related categories organized in a hierarchy or taxonomy has become a challenging problem. In this paper, we present a hierarchical and fine-tuning approach based on the Ordered Neural LSTM neural network, abbreviated as HFT-ONLSTM, for more accurate level-by-level HMTC. First, we present a novel approach to learning the joint embeddings based on parent category labels and textual data for accurately capturing the joint features of both category labels and texts. Second, a fine tuning technique is adopted for training parameters such that the text classification results in the upper level should contribute to the classification in the lower one. At last, the comprehensive analysis is made based on extensive experiments in comparison with the state-of-the-art hierarchical and flat multi-label text classification approaches over two benchmark datasets, and the experimental results show that our HFT-ONLSTM approach outperforms these approaches, in particular reducing computational costs while achieving superior performance.