A Sequential Graph Neural Network for Short Text Classification

Zhao, Ke; Huang, Lan; Song, Rui; Shen, Qiang; Xu, Hao

doi:10.3390/a14120352

Cited by 12 publications

(3 citation statements)

References 38 publications

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“…Zhao et al [24] proposed a novel approach to short text classification using a sequential graph neural network (SGNN). This aims to address the challenges of capturing the sequential dependencies and semantic relationships among words in short texts, which are often ignored by traditional methods.…”

Section: Applications Of Natural Language Processingmentioning

confidence: 99%

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

Deng,

Wang,

Wang

et al. 2023

BDCC

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Spain possesses a vast number of poems. Most have features that mean they present significantly different styles. A superficial reading of these poems may confuse readers due to their complexity. Therefore, it is of vital importance to classify the style of the poems in advance. Currently, poetry classification studies are mostly carried out manually, which creates extremely high requirements for the professional quality of classifiers and consumes a large amount of time. Furthermore, the objectivity of the classification cannot be guaranteed because of the influence of the classifier’s subjectivity. To solve these problems, a Spanish poetry classification framework was designed using artificial intelligence technology, which improves the accuracy, efficiency, and objectivity of classification. First, an artificial-intelligence-driven Spanish poetry classification framework is described in detail, and is illustrated by a framework diagram to clearly represent each step in the process. The framework includes many algorithms and models, such as the Term Frequency–Inverse Document Frequency (TF_IDF), Bagging, Support Vector Machines (SVMs), Adaptive Boosting (AdaBoost), logistic regression (LR), Gradient Boosting Decision Trees (GBDT), LightGBM (LGB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The roles of each algorithm in the framework are clearly defined. Finally, experiments were performed for model selection, comparing the results of these algorithms.The Bagging model stood out for its high accuracy, and the experimental results showed that the proposed framework can help researchers carry out poetry research work more efficiently, accurately, and objectively.

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Section: Applications Of Natural Language Processingmentioning

confidence: 99%

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

Deng,

Wang,

Wang

et al. 2023

BDCC

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show abstract

“…One of the RNN models is bidirectional long short-term memory (BiLSTM). BiLSTM is advantageous for sequence data modeling, however it cannot extract data characteristics in parallel [23]. Deep learning methods such as CNN and BiLSTM can be combined by taking advantage of the advantages of each method to obtain a more accurate deep learning model.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 classification using CNN-BiLSTM based on chest X-ray images

et al. 2023

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Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, Xception-BiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results.

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“…In conventional CNNs, vector z will be directly fed into the classifiers after the document representation is obtained, e.g., fully-connected neural networks [83,84]. From an architecture perspective, conventional sequence-based or convolutional neural networks that are often utilized for text classification are limited by their nature to prioritize sequentiality and locality [85,86]. While these deep learning models capture semantic and syntactic information in the Euclidean space and in local sequences well, they do not account for global word co-occurrences in a corpus that carries non-consecutive and long-distance semantics [39,87].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Domain-agnostic document and question classification using natural language processing techniques

Supraja¹

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I would like to dedicate this thesis to two special angels: God and my dear parents, without whom I could not have completed this challenging, yet fruitful PhD journey. I have no words to express my gratitude for every single thing my parents have done for my education and for my whole life. I cannot do anything to repay their love, affection, care, and concern for me.I would like to extend my sincere thanks to my husband, all my family members and friends who have supported me in one way or another. First and foremost, I would like to sincerely express my gratitude to my advisor A/Prof Andy W. H. Khong for his continuous support and guidance throughout my Ph.D. journey. His constant motivation and encouragement has transformed me tremendously.His advice has always been a great tonic to boost my morale and confidence level.

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A Sequential Graph Neural Network for Short Text Classification

Cited by 12 publications

References 38 publications

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

COVID-19 classification using CNN-BiLSTM based on chest X-ray images

Domain-agnostic document and question classification using natural language processing techniques

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