Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. In this work, to overcome such problems, we propose TextING for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are aggregated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.
Graph Alignment Topology as an Inductive Bias for Grounding Detection
Department of Pediatrics, University of Illinois College of Medicine Peoria（伊利诺伊大学皮奥里亚医学院儿科部）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机与数据科学学院）；Carle Illinois College of Medicine, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校卡莱医学院）
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent
Allen Institute for AI（艾伦人工智能研究所）；New York University（纽约大学）；University of Washington（华盛顿大学）；Bar Ilan University（巴伊兰大学）；Hebrew University of Jerusalem（希伯来大学）
Architecture-Agnostic Curriculum Learning for Document Understanding: Empirical Evidence from Text-Only and Multimodal
1 Synchromedia Laboratory, \' E cole de Technologie Sup\' e rieure (\' E TS), 1100 Notre-Dame St W, Montreal, QC H3C 1K3, Canada；2 Department of Computer Science, University of Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
Incremental Graph Construction Enables Robust Spectral Clustering of Texts
Jožef Stefan Institute and International Postgraduate School（乔泽夫·斯蒂芬研究所和国际研究生学院）；University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学学院）
