Text GCN-SW-KNN: a novel collaborative training multi-label classification method for WMS application themes by considering geographic semantics

Wei, Zhengyang; Gui, Zhipeng; Zhang, Min; Yang, Zelong; Mei, Yuao; Wu, Huayi; Liu, Hongbo; Yu, Jing

doi:10.1080/20964471.2021.1877434

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…This is also one of the two main indicators to achieve high accuracy and accuracy [9][10]. There are a variety of tools for text classification, such as text, pictures and videos according to the content, text in real space according to the different recognition fields can be divided into paper documents, and a computer vision algorithm including the characteristics of a defined conceptual structure unit is designed to extract and classify each type of information with specific purposes in the target corpus, Thus, correct conclusions can be drawn when the input and output results are achieved [11][12].…”

Section: Application Of Text Classificationmentioning

confidence: 99%

Automatic Text Classification Model Based on Machine Learning

2023

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The artificial neural network is composed of RBF algorithm, multi-layer parallel model and other parts. Its working principle is to classify the input image through layer by layer training, and then develop and determine the attributes of each neuron with similar features. This paper studies them based on Bayesian classifier, a common method in machine learning. First of all, before text recognition, we need to select the sample library and data type, and design the parameters according to the requirements to form an efficient automatic text classification. Then, according to the functional modules required by the specific model, select the appropriate language and compile and generate program code to realize the whole process, and carry out simulation tests on the functions of the model. The test results show that this test has prepared different quantities of simple words, short sentences, long articles, professional terms and colloquial expressions for classification tests. The classification accuracy of this model is as high as 90%, the error rate is low, and the classification time is fast, This shows that the model meets the needs of users.

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Section: Application Of Text Classificationmentioning

confidence: 99%

Automatic Text Classification Model Based on Machine Learning

2023

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“…The total number of nodes in every graph is the number of labels plus the number of unique words charge information [33]. We considered every word and label as a one-hot vector as the input model and used the identity matrix as the feature matrix [34]. The label-word edges can be built based on word occurrence in labels, and the word-word edges can be built based on word co-occurrence in the whole corpus.…”

Section: Charge Label Heterogeneous Graphmentioning

confidence: 99%

Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation

2021

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The task of charge prediction is to predict the charge based on the fact description. Existing methods have a good effect on the prediction of high-frequency charges, but the prediction of low-frequency charges is still a challenge. Moreover, there exist some confusing charges that have relatively similar fact descriptions, which can be easily misjudged. Therefore, we propose a model with data augmentation and feature augmentation for few-shot charge prediction. Specifically, the model takes the text description as the input and uses the Mixup method to generate virtual samples for data augmentation. Then, the charge information heterogeneous graph is introduced, and a novel graph convolutional network is designed to extract distinguishability features for feature augmentation. A feature fusion network is used to effectively integrate the charge graph knowledge into the fact to learn semantic-enhanced fact representation. Finally, the semantic-enhanced fact representation is used to predict the charge. In addition, based on the distribution of each charge, a category prior loss function is designed to increase the contribution of low-frequency charges to the model optimization. The experimental results on real-work datasets prove the effectiveness and robustness of the proposed model.

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“…Graph convolutional network (GCN) is a generalization of convolutional operations from structured mesh data to unstructured graph data [21], which has been extensively studied in many fields, such as traffic network prediction [22], simulation of multidrug side effects [23], and personnel reidentification [24]. In recent years, it has also been applied in text classification [25,26], and its effectiveness in Chinese text classification has been proved by some studies [27]. Yao et al [28] constructed text graphs based on word co-occurrence, using documents and words as nodes and GCN for semisupervised text classification, and obtained more advanced classification results in most types of texts.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Classification of Users’ Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network

Cheng¹,

Lin²

2023

JMIR Form Res

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Background Online medical and health communities provide a platform for internet users to share experiences and ask questions about medical and health issues. However, there are problems in these communities, such as the low accuracy of the classification of users’ questions and the uneven health literacy of users, which affect the accuracy of user retrieval and the professionalism of the medical personnel answering the question. In this context, it is essential to study more effective classification methods of users’ information needs. Objective Most online medical and health communities tend to provide only disease-type labels, which do not give a comprehensive summary of users’ needs. The study aims to construct a multilevel classification framework based on the graph convolutional network (GCN) model for users’ needs in online medical and health communities so that users can perform more targeted information retrieval. Methods Using the Chinese online medical and health community “Qiuyi” as an example, we crawled questions posted by users in the “Cardiovascular Disease” section as the data source. First, the disease types involved in the problem data were segmented by manual coding to generate the first-level label. Second, the needs were identified by K-means clustering to generate the users’ information needs label as the second-level label. Finally, by constructing a GCN model, users’ questions were automatically classified, thus realizing the multilevel classification of users’ needs. Results Based on the empirical research of questions posted by users in the “Cardiovascular Disease” section of Qiuyi, the hierarchical classification of users’ questions (data) was realized. The classification models designed in the study achieved accuracy, precision, recall, and F1-score of 0.6265, 0.6328, 0.5788, and 0.5912, respectively. Compared with the traditional machine learning method naïve Bayes and the deep learning method hierarchical text classification convolutional neural network, our classification model showed better performance. At the same time, we also performed a single-level classification experiment on users’ needs, which in comparison with the multilevel classification model exhibited a great improvement. Conclusions A multilevel classification framework has been designed based on the GCN model. The results demonstrated that the method is effective in classifying users’ information needs in online medical and health communities. At the same time, users with different diseases have different directions for information needs, which plays an important role in providing diversified and targeted services to the online medical and health community. Our method is also applicable to other similar disease classifications.

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Text GCN-SW-KNN: a novel collaborative training multi-label classification method for WMS application themes by considering geographic semantics

Cited by 10 publications

References 30 publications

Automatic Text Classification Model Based on Machine Learning

Automatic Text Classification Model Based on Machine Learning

Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation

Multilevel Classification of Users’ Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network

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