Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification

Yaqing Wang, Song Wang, Quanming Yao, Dejing Dou


Abstract
Short text classification is a fundamental task in natural language processing. It is hard due to the lack of context information and labeled data in practice. In this paper, we propose a new method called SHINE, which is based on graph neural network (GNN), for short text classification. First, we model the short text dataset as a hierarchical heterogeneous graph consisting of word-level component graphs which introduce more semantic and syntactic information. Then, we dynamically learn a short document graph that facilitates effective label propagation among similar short texts. Thus, comparing with existing GNN-based methods, SHINE can better exploit interactions between nodes of the same types and capture similarities between short texts. Extensive experiments on various benchmark short text datasets show that SHINE consistently outperforms state-of-the-art methods, especially with fewer labels.
Anthology ID:
2021.emnlp-main.247
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3091–3101
Language:
URL:
https://aclanthology.org/2021.emnlp-main.247
DOI:
10.18653/v1/2021.emnlp-main.247
Bibkey:
Cite (ACL):
Yaqing Wang, Song Wang, Quanming Yao, and Dejing Dou. 2021. Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3091–3101, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification (Wang et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.247.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.247.mp4
Code
 tata1661/shine-emnlp21