GAINER: Graph Machine Learning with Node-specific Radius for Classification of Short Texts and Documents (2024.eacl-long)
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| Challenge: | Recent advances in Graph Machine Learning (GML) have led to the development of numerous models tailored for processing text for various natural language applications. |
| Approach: | They propose a framework called Graph mAchine learnIng with Node-spEcific Radius that is aimed at graph-based NLP. |
| Outcome: | The proposed framework is non-neural and novel for graph-based NLP. |
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Graph-based Deep Learning in Natural Language Processing (D19-2)
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| Challenge: | This tutorial aims to introduce graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP) |
| Approach: | It provides a brief introduction to graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). |
| Outcome: | This tutorial provides a brief introduction to graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for natural language processing (NLP). |
Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)
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| Challenge: | Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics. |
| Approach: | They compare graph representation methods for text classification using different architectures and setups. |
| Outcome: | The proposed graph representation methods outperform other models in document comprehension tasks. |
Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)
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| Challenge: | Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks. |
| Approach: | This tutorial will cover applying deep learning on graph techniques to NLP using Graph Neural Networks (GNNs) Graph4NLP is the first library for researchers and practitioners for easy use of GNNs for various NLP tasks. |
| Outcome: | This tutorial will cover the latest developments in deep learning on graph techniques and their applications in various NLP tasks. |
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)
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| Challenge: | Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words. |
| Approach: | They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four benchmark datasets. |
Text Graph Transformer for Document Classification (2020.emnlp-main)
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| Challenge: | Existing methods for text classification are not scalable to large corpus and ignore heterogeneity of text graph. |
| Approach: | They propose a Transformer-based heterogeneous graph neural network that captures structure and heterogenity from the text graph. |
| Outcome: | The proposed model outperforms state-of-the-art methods on large-sized corpus datasets and significantly reduces computing and memory costs. |
How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
| Challenge: | Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs). |
| Approach: | They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications. |
| Outcome: | The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication. |
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)
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| Challenge: | TextGraphs is a workshop on graph-based methods for natural language processing . the workshop is being organized in conjunction with the 9th International Joint Conference on Natural Language Processing . |
| Approach: | TextGraphs is the 13th edition of the Workshop on Graph-Based Methods for Natural Language Processing . the workshop promotes synergy between GT and natural language processing . |
| Outcome: | the 2013 edition of TextGraphs is being held in conjunction with the 9th International Joint Conference on Natural Language Processing in Hong Kong. |
Text Level Graph Neural Network for Text Classification (D19-1)
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| Challenge: | Recent researches have explored graph neural network (GNN) techniques on text classification, but they are faced with the problems of fixed corpus level graph structure which don’t support online testing and high memory consumption. |
| Approach: | They propose a graph neural network model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. |
| Outcome: | The proposed model outperforms existing models on several text classification datasets even with consuming less memory. |
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (2020.tacl-1)
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| Challenge: | Recent graph-to-text models generate text from graph data using global or local aggregation . global node encoding allows explicit communication between two distant nodes, but fails to capture long-range relationships. |
| Approach: | They propose to combine global and local aggregation to learn node representations . they propose to use global and locally encoding to learn contextualized node embeddings based on graph data . |
| Outcome: | The proposed models outperform state-of-the-art models on two graph-to-text datasets by 18.01 and 63.69 points. |
The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction (2020.findings-emnlp)
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| Challenge: | Existing methods for document-level relation extraction do not capture the representations of the nodes in the graphs. |
| Approach: | They propose to explicitly compute the representations for the nodes in the graph-based edge-oriented model for Document-level Relation Extraction (DRE) . they propose to introduce two novel representation regularization mechanisms to improve the representation vectors for DRE. |
| Outcome: | The proposed model achieves state-of-the-art performance on two benchmark datasets. |