Abstract
Text classification is a primary task in natural language processing (NLP). Recently, graph neural networks (GNNs) have developed rapidly and been applied to text classification tasks. As a special kind of graph data, the tree has a simpler data structure and can provide rich hierarchical information for text classification. Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification. Specifically, we first establish a dependency parsing graph for each text. Then we designed a structural entropy minimization algorithm to decode the key information in the graph and convert each graph to its corresponding coding tree. Based on the hierarchical structure of the coding tree, the representation of the entire graph is obtained by updating the representation of non-leaf nodes in the coding tree layer by layer. Finally, we present the effectiveness of hierarchical information in text classification. Experimental results show that HINT outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters.- Anthology ID:
- 2022.coling-1.79
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 950–959
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.79
- DOI:
- Cite (ACL):
- Chong Zhang, He Zhu, Xingyu Peng, Junran Wu, and Ke Xu. 2022. Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network. In Proceedings of the 29th International Conference on Computational Linguistics, pages 950–959, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (Zhang et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.coling-1.79.pdf
- Code
- daisean/hint + additional community code