HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification

TL;DR

This paper introduces HFT-ONLSTM, a hierarchical neural network model that improves multi-label text classification accuracy in hierarchical categories by joint embeddings and fine-tuning, outperforming state-of-the-art methods.

Contribution

The paper proposes a novel hierarchical and fine-tuning approach using Ordered Neural LSTM for more accurate level-by-level multi-label text classification.

Findings

Outperforms existing hierarchical and flat classification methods.

Reduces computational costs while maintaining high accuracy.

Effective joint embedding of category labels and texts.

Abstract

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.