A self-training hierarchical prototype-based approach for semi-supervised classification

Gu, Xiaowei

doi:10.1016/j.ins.2020.05.018

Cited by 34 publications

(38 citation statements)

References 36 publications

(59 reference statements)

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“…However, there are less common machine learning approaches like unsupervised learning, which have been used in other fields, e.g. for detecting spammer groups [128] and for rumour detection [129], [130], or semi-supervised machine learning models [131], [132]. These unconventional methods have also been used for cyberbullying detection.…”

Section: ) Unconventional Modelsmentioning

confidence: 99%

When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection

et al. 2021

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Web 2.0 helped user-generated platforms to spread widely. Unfortunately, it also allowed for cyberbullying to spread. Cyberbullying has negative effects that could lead to cases of depression and low self-esteem. It has become crucial to develop tools for automated cyberbullying detection. The research on developing these tools has been growing over the last decade, especially with the recent advances in machine learning and natural language processing. Given the large body of work on this topic, it is vital to critically review the literature on cyberbullying within the context of these latest advances. In this paper, we conduct a survey on the automated detection of cyberbullying. Our survey sheds light on some challenges and limitations for the field. The challenges range from defining cyberbullying, data collection and feature representation to model selection, training and evaluation. We also provide some suggestions for improving the task of cyberbullying detection. In addition to the survey, we propose to improve the task of cyberbullying detection by addressing some of the raised limitations: 1) Using recent contextual language models like BERT for the detection of cyberbullying; 2) Using slang-based word embeddings to generate better representations of the cyberbullying-related datasets. Our results show that BERT outperforms state-of-the-art cyberbullying detection models and deep learning models. The results also show that deep learning models initialised with slang-based word embeddings outperform deep learning models initialised with traditional word embeddings.

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Section: ) Unconventional Modelsmentioning

confidence: 99%

When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection

et al. 2021

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“…Figure 5 displays the self-training process of the CRF model. The essence of self-training (Gu, 2020) is to find a way to expand the labelled dataset with unlabelled data. As a method that can achieve semi-supervised learning, the self-training algorithm can not only lessen the laborious workload of annotating data manually, but also enhance the generalisation performance of the CRF model.…”

Section: The Self-training Of the Crf Modelmentioning

confidence: 99%

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Zhang

Zhu

et al. 2021

Connection Science

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Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results.

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“…Salazar et al [21] investigated the performance of semi-supervised learning in imbalanced classification problems and analyzed the effect of data augmentation in several simulated and experimental scenarios of automatic credit card fraud detection. Gu et al [22] proposed a selftraining hierarchical prototype-based model, which recognizes prototypes according to the multigranularity levels of labeled data. Furthermore, new patterns are recognized from unlabeled data, and key information is mined for classification with pseudo label methods.…”

Section: Introductionmentioning

confidence: 99%

Weighted Pseudo Labeled Data and Mutual Learning for Semi-Supervised Classification

Gan

Yuan

2021

IEEE Access

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In this paper, a semi-supervised classification algorithm that is based on weighted pseudo labeled data and mutual learning is proposed. The purpose of our method is to improve the classification performance of semi-supervised learning models and rectify the incorrect pseudo labels in the process of training. Specifically, the algorithm is built with a deep convolutional neural network and an ensemble learning model. First, output smearing is employed to construct different training sets and perform model initialization. The pseudo labels of unlabeled data are inferred by network predictions. Second, based on selection and weighting strategies for pseudo labeled data, pseudo labeled data with high confidence are selected and added to the real labeled training set. Accordingly, the model is retrained on the weighted pseudo labeled data. Last, a mutual learning strategy is applied to enhance the prediction consistency among classifiers. Furthermore, diversity fine tuning and mutual learning are performed alternately to determine the optimal balance between diversity and consistency, which consequently improves the accuracy of the pseudo label predictions. The experimental results on three benchmark datasets, namely, MNIST, CIFAR10 and SVHN, demonstrate that the proposed method effectively rectifies the incorrect pseudo labels. Notably, the method achieves the best performance compared with state-of-the-art semisupervised classification methods.INDEX TERMS Image classification, semi-supervised learning, weighted pseudo labeled data, mutual learning.

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A self-training hierarchical prototype-based approach for semi-supervised classification

Cited by 34 publications

References 36 publications

When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection

When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Weighted Pseudo Labeled Data and Mutual Learning for Semi-Supervised Classification

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