A Novel Graph-Sequence Learning Model for Inductive Text Classification
Text classification plays an important role in various downstream text-related tasks, such as sentiment analysis, fake news detection, and public opinion analysis. Recently, text classification based on Graph Neural Networks (GNNs) has made significant progress due to their strong capabilities of structural relationship learning. However, these approaches still face two major limitations. First, these approaches fail to fully consider the diverse structural information across word pairs, e.g., co-occurrence, syntax, and semantics. Furthermore, they neglect sequence information in the text graph structure information learning module and can not classify texts with new words and relations. In this paper, we propose a Novel Graph-Sequence Learning Model for Inductive Text Classification (TextGSL) to address the previously mentioned issues. More specifically, we construct a single text-level graph for all words in each text and establish different edge types based on the diverse relationships between word pairs. Building upon this, we design an adaptive multi-edge message-passing paradigm to aggregate diverse structural information between word pairs. Additionally, sequential information among text data can be captured by the proposed TextGSL through the incorporation of Transformer layers. Therefore, TextGSL can learn more discriminative text representations. TextGSL has been comprehensively compared with several strong baselines. The experimental results on diverse benchmarking datasets demonstrate that TextGSL outperforms these baselines in terms of accuracy.
Code (0)
등록된 구현이 없습니다.
Tasks
Fake News DetectionText ClassificationSentiment AnalysisSimilar Papers 제목 키워드 기반
Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP
Graph neural networks have triggered a resurgence of graph-based text classification methods, defining today's state of the art. We show that a wide multi-layer perceptron (MLP) using a Bag-of-Words (BoW) outperforms the…
text-classificationText ClassificationInducT-GCN: Inductive Graph Convolutional Networks for Text Classification
Text classification aims to assign labels to textual units by making use of global information. Recent studies have applied graph neural network (GNN) techniques to capture the global word co-occurrence in a corpus. Most…
ClassificationGraph Neural Networktext-classificationText ClassificationInducT-GCN: Inductive Graph Convolutional Networks for Text Classification
Text classification aims to assign labels to textual units by making use of global information. Recent studies have applied graph neural network (GNN) to capture the global word co-occurrence in a corpus. Existing approa…
ClassificationGraph Neural Networktext-classificationText ClassificationEvery Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
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 …
ClassificationDocument EmbeddingGeneral ClassificationInductive Learning+2ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification
Graph neural networks (GNNs) have been recently applied in natural language processing. Various GNN research studies are proposed to learn node interactions within the local graph of each document that contains words, se…
text-classificationText ClassificationWord Embeddings