NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
Fuhan Cai, Duo Liu, Zhongqiang Zhang, Ge Liu, Xiaozhe Yang, Xiangzhong Fang
Abstract
Hierarchical text classification (HTC) is a significant but challenging task in natural language processing (NLP) due to its complex taxonomic label hierarchy. Recently, there have been a number of approaches that applied prompt learning to HTC problems, demonstrating impressive efficacy. The majority of prompt-based studies emphasize global hierarchical features by employing graph networks to represent the hierarchical structure as a whole, with limited research on maintaining path consistency within the internal hierarchy of the structure. In this paper, we formulate prompt-based HTC as a named entity recognition (NER) task and introduce conditional random fields (CRF) and Global Pointer to establish hierarchical dependencies. Specifically, we approach single- and multi-path HTC as flat and nested entity recognition tasks and model them using span- and token-based methods. By narrowing the gap between HTC and NER, we maintain the consistency of internal paths within the hierarchical structure through a simple and effective way. Extensive experiments on three public datasets show that our method achieves state-of-the-art (SoTA) performance.- Anthology ID:
- 2024.lrec-main.1060
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 12117–12126
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1060
- DOI:
- Cite (ACL):
- Fuhan Cai, Duo Liu, Zhongqiang Zhang, Ge Liu, Xiaozhe Yang, and Xiangzhong Fang. 2024. NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12117–12126, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (Cai et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1060.pdf