Affiliation:
1. Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China
Abstract
Hierarchical multi-label text classification (HMTC) is a highly relevant and widely discussed topic in the era of big data, particularly for efficiently classifying extensive amounts of text data. This study proposes the HTMC-PGT framework for poverty governance’s single-path hierarchical multi-label classification problem. The framework simplifies the HMTC problem into training and combination problems of multi-class classifiers in the classifier tree. Each independent classifier in this framework uses an XLNet pretrained model to extract char-level semantic embeddings of text and employs a hierarchical attention mechanism integrated with Bi-LSTM (BiLSTM + HA) to extract semantic embeddings at the document level for classification purposes. Simultaneously, this study proposes that the structure uses transfer learning (TL) between classifiers in the classifier tree. The experimental results show that the proposed XLNet + BiLSTM + HA + FC + TL model achieves micro-P, micro-R, and micro-F1 values of 96.1%, which is 7.5~38.1% higher than those of other baseline models. The HTMC-PGT framework based on XLNet, BiLSTM + HA, and transfer learning (TL) between classifier tree nodes proposed in this study solves the hierarchical multi-label classification problem of poverty governance text (PGT). It provides a new idea for solving the traditional HMTC problem.
Funder
National Key R&D Program of China
Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference43 articles.
1. Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings?;Wahl;BMJ Glob. Health,2018
2. A review of explainable AI in the satellite data, deep machine learning, and human poverty domain;Hall;Patterns,2022
3. Usmanova, A., Aziz, A., Rakhmonov, D., and Osamy, W. (2022). Utilities of Artificial Intelligence in Poverty Prediction: A Review. Sustainability, 14.
4. Mechanism and Path to Improve Digital Governance Capacity for Prevention of Relapse into Poverty Based on the Integration of New IT;Wang;J. Huazhong Agric. Univ. Soc. Sci. Ed.,2023
5. Collaborating with the Machines: A Hybrid Method for Classifying Policy Documents;Loftis;Policy Stud. J.,2020
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