Heterogeneous Graph Neural Networks for Multi-label Text Classification

• URL: http://arxiv.org/abs/2103.14620v1
• Date: Fri, 26 Mar 2021 17:33:31 GMT
• Title: Heterogeneous Graph Neural Networks for Multi-label Text Classification
• Authors: Irene Li, Tianxiao Li, Yixin Li, Ruihai Dong, and Toyotaro Suzumura
• Abstract summary: Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP) We propose a heterogeneous graph convolutional network model to solve the MLTC problem by modeling tokens and labels as nodes in a heterogeneous graph. We evaluate our method on three real-world datasets and the experimental results show that it achieves significant improvements and outperforms state-of-the-art comparison methods.
• Score: 5.290920289670573
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP). Compared with single-label text classification, MLTC has a wider range of applications in practice. In this paper, we propose a heterogeneous graph convolutional network model to solve the MLTC problem by modeling tokens and labels as nodes in a heterogeneous graph. In this way, we are able to take into account multiple relationships including token-level relationships. Besides, the model allows a good explainability as the token-label edges are exposed. We evaluate our method on three real-world datasets and the experimental results show that it achieves significant improvements and outperforms state-of-the-art comparison methods.

Related papers

• A data-centric approach for assessing progress of Graph Neural Networks [7.2249434861826325]
  Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. Most improvements are in multi-class classification, with less focus on the cases where each node could have multiple labels. First challenge in studying multi-label node classification is the scarcity of publicly available datasets.
  arXiv  Detail & Related papers  (2024-06-18T09:41:40Z)
• Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning [78.49090351193269]
  We propose a novel graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis. Specifically, we formulate the WSI as a heterogeneous graph with "nucleus-type" attribute to each node and a semantic attribute similarity to each edge. Our framework outperforms the state-of-the-art methods with considerable margins on various tasks.
  arXiv  Detail & Related papers  (2023-07-09T14:43:40Z)
• Deep Partial Multi-Label Learning with Graph Disambiguation [27.908565535292723]
  We propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN) Specifically, we introduce the instance-level and label-level similarities to recover label confidences. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels.
  arXiv  Detail & Related papers  (2023-05-10T04:02:08Z)
• Multi-label Node Classification On Graph-Structured Data [7.892731722253387]
  Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. A more general and realistic scenario in which each node could have multiple labels has so far received little attention. We collect and release three real-world biological datasets and develop a multi-label graph generator.
  arXiv  Detail & Related papers  (2023-04-20T15:34:20Z)
• Graph Attention Transformer Network for Multi-Label Image Classification [50.0297353509294]
  We propose a general framework for multi-label image classification that can effectively mine complex inter-label relationships. Our proposed methods can achieve state-of-the-art performance on three datasets.
  arXiv  Detail & Related papers  (2022-03-08T12:39:05Z)
• Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification [60.233529926965836]
  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. Then, we dynamically learn a short document graph that facilitates effective label propagation among similar short texts.
  arXiv  Detail & Related papers  (2021-10-30T05:33:05Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• Instance-Aware Graph Convolutional Network for Multi-Label Classification [55.131166957803345]
  Graph convolutional neural network (GCN) has effectively boosted the multi-label image recognition task. We propose an instance-aware graph convolutional neural network (IA-GCN) framework for multi-label classification.
  arXiv  Detail & Related papers  (2020-08-19T12:49:28Z)
• Sequential Graph Convolutional Network for Active Learning [53.99104862192055]
  We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN) With a small number of randomly sampled images as seed labelled examples, we learn the parameters of the graph to distinguish labelled vs unlabelled nodes. We exploit these characteristics of GCN to select the unlabelled examples which are sufficiently different from labelled ones.
  arXiv  Detail & Related papers  (2020-06-18T00:55:10Z)
• Multi-Label Text Classification using Attention-based Graph Neural Network [0.0]
  A graph attention network-based model is proposed to capture the attentive dependency structure among the labels. The proposed model achieves similar or better performance compared to the previous state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-22T17:12:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.