Hierarchical Comprehensive Context Modeling for Chinese Text Classification

Jin-gang, Liu; Xia, Chunhe; Yan, Haihua; Xie, Zhipu; Sun, Jie

doi:10.1109/access.2019.2949175

Cited by 16 publications

(4 citation statements)

References 36 publications

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“…COVID-DeepPredictor based on Long-Short Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997 ; Tang et al, 2019 ) is developed. Though, LSTM has been profusely used in many works for text classification (Jin et al, 2019 ; Liu et al, 2019 ; Zhang et al, 2019 ), to the best of the authors' knowledge, this is the first attempt to use LSTM for the prediction of SARS-CoV-2 using genomic sequences of virus considering alignment-free approach. For this purpose, k -mer technique is used to generate Bag-of-Descriptors (BoDs) and consequently Bag-of-Unique-Descriptors (BoUDs) as vocabulary.…”

Section: Introductionmentioning

confidence: 99%

COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses

et al. 2021

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The COVID-19 disease for Novel coronavirus (SARS-CoV-2) has turned out to be a global pandemic. The high transmission rate of this pathogenic virus demands an early prediction and proper identification for the subsequent treatment. However, polymorphic nature of this virus allows it to adapt and sustain in different kinds of environment which makes it difficult to predict. On the other hand, there are other pathogens like SARS-CoV-1, MERS-CoV, Ebola, Dengue, and Influenza as well, so that a predictor is highly required to distinguish them with the use of their genomic information. To mitigate this problem, in this work COVID-DeepPredictor is proposed on the framework of deep learning to identify an unknown sequence of these pathogens. COVID-DeepPredictor uses Long Short Term Memory as Recurrent Neural Network for the underlying prediction with an alignment-free technique. In this regard, k-mer technique is applied to create Bag-of-Descriptors (BoDs) in order to generate Bag-of-Unique-Descriptors (BoUDs) as vocabulary and subsequently embedded representation is prepared for the given virus sequences. This predictor is not only validated for the dataset using K-fold cross-validation but also for unseen test datasets of SARS-CoV-2 sequences and sequences from other viruses as well. To verify the efficacy of COVID-DeepPredictor, it has been compared with other state-of-the-art prediction techniques based on Linear Discriminant Analysis, Random Forests, and Gradient Boosting Method. COVID-DeepPredictor achieves 100% prediction accuracy on validation dataset while on test datasets, the accuracy ranges from 99.51 to 99.94%. It shows superior results over other prediction techniques as well. In addition to this, accuracy and runtime of COVID-DeepPredictor are considered simultaneously to determine the value of k in k-mer, a comparative study among k values in k-mer, Bag-of-Descriptors (BoDs), and Bag-of-Unique-Descriptors (BoUDs) and a comparison between COVID-DeepPredictor and Nucleotide BLAST have also been performed. The code, training, and test datasets used for COVID-DeepPredictor are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-DeepPredictor/.

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Section: Introductionmentioning

confidence: 99%

COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses

et al. 2021

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“…Before delving into an examination of the proposed approach, it would be worthwhile to briefly review the methods that were previously introduced in this context. Xie, et al (2019) employed LSTM to extract context and sequence characteristics from Chinese news text for classification purposes (Xie, et al, 2019), while Liu, et al (2019) presented a hierarchical model that combined LSTM and temporal convolutional networks to extract context and information sequentially (Liu, et al, 2019). Chen, Cong and Lv (2022) introduced a local feature convolution network based on BERT to capture local features and address the characteristics of lengthy Chinese news texts with significant amounts of information (Chen, Cong and Lv, 2022).…”

Section: Related Workmentioning

confidence: 99%

Bridging the Gap

Badawi

2024

ARO

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Effective organization and retrieval of news content are heavily reliant on accurate news classification. While the mountainous research has been conducted in resourceful languages like English and Chinese, the researches on under-resourced languages like the Kurdish language are severely lacking. To address this challenge, we introduce a hybrid approach called RFO-CNN in this paper. The proposed method combines an improved version of red fox optimization algorithm (RFO) and convolutional neural network (CNN) for finetuning CNN’s parameters. Our model’s efficacy was tested on two widely used Kurdish news datasets, KNDH and KDC-4007, both of which contain news articles classified into various categories. We compared the performance of RFO-CNN to other cutting-edge deep learning models such as bidirectional long short-term memory networks and bidirectional encoder representations from transformers (BERT) transformers, as well as classical machine learning approaches such as multinomial naive bayes, support vector machine, and K-nearest neighbors. We trained and tested our datasets using four different scenarios: 60:40, 70:30, 80:20, and 90:10. Our experimental results demonstrate the superiority of the RFO-CNN model across all scenarios, outperforming the benchmark BERT model and other machine learning models in terms of accuracy and F1-score.

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“…Even recently, a considerable number of researchers have inclined towards solving Chinese text classification without considering internal features within Chinese texts and have focused on complex deep network architectures [26,27]. However, the application of Chinese representations using glyph features has been a long-standing research topic.…”

Section: Research On Chinese Text Representationmentioning

confidence: 99%

Assemble the shallow or integrate a deep? Toward a lightweight solution for glyph-aware Chinese text classification

Hou

Wang

2023

PLoS ONE

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As hieroglyphic languages, such as Chinese, differ from alphabetic languages, researchers have always been interested in using internal glyph features to enhance semantic representation. However, the models used in such studies are becoming increasingly computationally expensive, even for simple tasks like text classification. In this paper, we aim to balance model performance and computation cost in glyph-aware Chinese text classification tasks. To address this issue, we propose a lightweight ensemble learning method for glyph-aware Chinese text classification (LEGACT) that consists of typical shallow networks as base learners and machine learning classifiers as meta-learners. Through model design and a series of experiments, we demonstrate that an ensemble approach integrating shallow neural networks can achieve comparable results even when compared to large-scale transformer models. The contribution of this paper includes a lightweight yet powerful solution for glyph-aware Chinese text classification and empirical evidence of the significance of glyph features for hieroglyphic language representation. Moreover, this paper emphasizes the importance of assembling shallow neural networks with proper ensemble strategies to reduce computational workload in predictive tasks.

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Hierarchical Comprehensive Context Modeling for Chinese Text Classification

Cited by 16 publications

References 36 publications

COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses

COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses

Bridging the Gap

Assemble the shallow or integrate a deep? Toward a lightweight solution for glyph-aware Chinese text classification

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