A Text Classification Model Based on GCN and BiGRU Fusion

Dong, Yonghao; Yang, Zhenmin; Cao, Hui

doi:10.1145/3532213.3532260

Cited by 6 publications

(1 citation statement)

References 2 publications

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“…A text classification model with convolutional neural networks such as Graphical Neural Network (GCN) and Bidirectional Recursive Unit (Bi-GRU) is exposed in [23]. The model was designed to address the lack of ability of neural networks to capture contextual semantics.…”

Section: Related Workmentioning

confidence: 99%

Semantic Relationship-based Embedding Models for Text Classification

Lezama-Sánchez¹,

Vidal²,

Reyes‐Ortiz³

2022

Preprint

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Embedding representation models characterize each word as a vector of numbers with a fixed length. These models have been used in tasks involving text classification, such as recommen- dation and question-answer systems. Semantic relationships are words with a relationship between them providing a complete idea to a text. Therefore, it is hypothesized that an embedding model involving semantic relationships will provide better performance for tasks that use them. This paper presents three embedding models based on semantic relations extracted fromWikipedia to classify texts. The synonym, hyponym, and hyperonym semantic relationships were the ones considered in this work since previous experiments have shown that they are the ones that provide the most semantic knowledge. Lexical-syntactic patterns present in the literature were implemented and subsequently applied to the Wikipedia corpus to obtain the semantic relationships present in it. Several semantic relationships are used in different models: synonymy, hyponym-hyperonym, and a combination of the first two. A convolutional neural network was trained for text classification to evaluate the performance of each model. The results obtained were evaluated with the metrics of precision, accuracy, recall, and F1-measure. The best values obtained with the second model were accuracy of 0.79 for the 20-Newsgroup corpus. F1-measure and recall of 0.87 respectively for the Reuters corpus.

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Section: Related Workmentioning

confidence: 99%

Semantic Relationship-based Embedding Models for Text Classification

Lezama-Sánchez¹,

Vidal²,

Reyes‐Ortiz³

2022

Preprint

View full text Add to dashboard Cite

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An effective multi-modal adaptive contextual feature information fusion method for Chinese long text classification

Xu,

Liu,

Zhang

et al. 2024

Artif Intell Rev

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Chinese long text classification plays a vital role in Natural Language Processing. Compared to Chinese short texts, Chinese long texts contain more complex semantic feature information. Furthermore, the distribution of these semantic features is uneven due to the varying lengths of the texts. Current research on Chinese long text classification models primarily focuses on enhancing text semantic features and representing Chinese long texts as graph-structured data. Nonetheless, these methods are still susceptible to noise information and tend to overlook the deep semantic information in long texts. To address the above challenges, this study proposes a novel and effective method called MACFM, which introduces a deep feature information mining method and an adaptive modal feature information fusion strategy to learn the semantic features of Chinese long texts thoroughly. First, we present the DCAM module to capture complex semantic features in Chinese long texts, allowing the model to learn detailed high-level representation features. Then, we explore the relationships between word vectors and text graphs, enabling the model to capture abundant semantic information and text positional information from the graph. Finally, we develop the AMFM module to effectively combine different modal feature representations and eliminate the unrelated noise information. The experimental results on five Chinese long text datasets show that our method significantly improves the accuracy of Chinese long text classification tasks. Furthermore, the generalization experiments on five English datasets and the visualized results demonstrate the effectiveness and interpretability of the MACFM model.

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Transformer and Graph Convolutional Network for Text Classification

Liu,

Guan,

Yang

et al. 2023

Int J Comput Intell Syst

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Graph convolutional network (GCN) is an effective tool for feature clustering. However, in the text classification task, the traditional TextGCN (GCN for Text Classification) ignores the context word order of the text. In addition, TextGCN constructs the text graph only according to the context relationship, so it is difficult for the word nodes to learn an effective semantic representation. Based on this, this paper proposes a text classification method that combines Transformer and GCN. To improve the semantic accuracy of word node features, we add a part of speech (POS) to the word-document graph and build edges between words based on POS. In the layer-to-layer of GCN, the Transformer is used to extract the contextual and sequential information of the text. We conducted the experiment on five representative datasets. The results show that our method can effectively improve the accuracy of text classification and is better than the comparison method.

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A Text Classification Model Based on GCN and BiGRU Fusion

Cited by 6 publications

References 2 publications

Semantic Relationship-based Embedding Models for Text Classification

Semantic Relationship-based Embedding Models for Text Classification

An effective multi-modal adaptive contextual feature information fusion method for Chinese long text classification

Transformer and Graph Convolutional Network for Text Classification

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