%0 Conference Proceedings %T Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network %A Lyu, Chen %A Liu, Weijie %A Wang, Ping %Y Scott, Donia %Y Bel, Nuria %Y Zong, Chengqing %S Proceedings of the 28th International Conference on Computational Linguistics %D 2020 %8 December %I International Committee on Computational Linguistics %C Barcelona, Spain (Online) %F lyu-etal-2020-shot %X In this paper, we propose a new few-shot text classification method. Compared with supervised learning methods which require a large corpus of labeled documents, our method aims to make it possible to classify unlabeled text with few labeled data. To achieve this goal, we take advantage of advanced pre-trained language model to extract the semantic features of each document. Furthermore, we utilize an edge-labeling graph neural network to implicitly models the intra-cluster similarity and the inter-cluster dissimilarity of the documents. Finally, we take the results of the graph neural network as the input of a prototypical network to classify the unlabeled texts. We verify the effectiveness of our method on a sentiment analysis dataset and a relation classification dataset and achieve the state-of-the-art performance on both tasks. %R 10.18653/v1/2020.coling-main.485 %U https://aclanthology.org/2020.coling-main.485 %U https://doi.org/10.18653/v1/2020.coling-main.485 %P 5547-5552