[Retracted] News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model

Sun, Ningfeng; Du, Chengye

doi:10.1155/2021/8064579

Cited by 19 publications

(10 citation statements)

References 26 publications

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“…The research emphasizes the significance of data classification in making online news sources more userfriendly. The paper [25] examines the history and current state of text classification research, and it makes projections about where the discipline is headed in terms of both research priorities and new frontiers. Next, they build a text categorization model for network news using deep learning and explain how each component works.…”

Section: Related Workmentioning

confidence: 99%

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

2023

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The study focuses on news category prediction and investigates the performance of sentence embedding of four transformer models (BERT, RoBERTa, MPNet, and T5) and their variants as feature vectors when combined with Softmax and Random Forest using two accessible news datasets from Kaggle. The data are stratified into train and test sets to ensure equal representation of each category. Word embeddings are generated using transformer models, with the last hidden layer selected as the embedding. Mean pooling calculates a single vector representation called sentence embedding, capturing the overall meaning of the news article. The performance of Softmax and Random Forest, as well as the soft voting of both, is evaluated using evaluation measures such as accuracy, F1 score, precision, and recall. The study also contributes by evaluating the performance of Softmax and Random Forest individually. The macro-average F1 score is calculated to compare the performance of different transformer embeddings in the same experimental settings. The experiments reveal that MPNet versions v1 and v3 achieve the highest F1 score of 97.7% when combined with Random Forest, while T5 Large embedding achieves the highest F1 score of 98.2% when used with Softmax regression. MPNet v1 performs exceptionally well when used in the voting classifier, obtaining an impressive F1 score of 98.6%. In conclusion, the experiments validate the superiority of certain transformer models, such as MPNet v1, MPNet v3, and DistilRoBERTa, when used to calculate sentence embeddings within the Random Forest framework. The results also highlight the promising performance of T5 Large and RoBERTa Large in voting of Softmax regression and Random Forest. The voting classifier, employing transformer embeddings and ensemble learning techniques, consistently outperforms other baselines and individual algorithms. These findings emphasize the effectiveness of the voting classifier with transformer embeddings in achieving accurate and reliable predictions for news category classification tasks.

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Section: Related Workmentioning

confidence: 99%

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

2023

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“…But according to previous research literature reviews SVM [14] is most frequently used classifier algorithm. In [15] proposed a news article classification model framework based on deep hybrid learning and compared it to traditional text classification to demonstrate the superiority of network news text classification, and it outperforms the standardized method for classifying news texts in terms of overall performance. In [16] showed a comparative analysis among DT, KNN, NB, and Rocchio's algorithm where their studies said that SVM outperforms better than all other classifiers.…”

Section: Introductionmentioning

confidence: 99%

Comparison analysis of Bangla news articles classification using support vector machine and logistic regression

et al. 2023

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In the information age, Bangla news articles on the internet are fast-growing. For organizing, every news site has a particular structure and categorization. News article classification is a method to determine a document's classification based on various predefined categories. This research discusses the classification of Bangla news articles on the online platform and tries to make constructive comparison using several classification algorithms. For Bangla news articles classification, term frequencyinverse document frequency (TF-IDF) weighting and count vectorizer have been used as a feature extraction process, and two common classifiers named support vector machine (SVM) and logistic regression (LR) employed for classifying the documents. It is clear that the accuracy of the experimental results by applying SVM is 84.0% and LR is 81.0% for twelve categories of news articles. In this research work, when we have made comparison two renowned classification algorithms applied on the Bangla news articles, LR was outperformed by SVM.

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“…The four vowel letters are " ", " ", " ", and " ". Vowel letters must be added to the upper or lower part of consonant letters and cannot be written independently [13] . Two kinds of characters are derived from the consonant letters: the superfix and the subfix.…”

Section: Introductionmentioning

confidence: 99%

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

Gao

et al. 2023

Tsinghua Sci. Technol.

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Sentence Boundary Disambiguation (SBD) is a preprocessing step for natural language processing.Segmenting text into sentences is essential for Deep Learning (DL) and pretraining language models. Tibetan punctuation marks may involve ambiguity about the sentences' beginnings and endings. Hence, the ambiguous punctuation marks must be distinguished, and the sentence structure must be correctly encoded in language models. This study proposed a component-level Tibetan SBD approach based on the DL model. The models can reduce the error amplification caused by word segmentation and part-of-speech tagging. Although most SBD methods have only considered text on the left side of punctuation marks, this study considers the text on both sides. In this study, 465 669 Tibetan sentences are adopted, and a Bidirectional Long Short-Term Memory (Bi-LSTM) model is used to perform SBD. The experimental results show that the F1-score of the Bi-LSTM model reached 96%, the most efficient among the six models. Experiments are performed on low-resource languages such as Turkish and Romanian, and high-resource languages such as English and German, to verify the models' generalization.

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[Retracted] News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model

Cited by 19 publications

References 26 publications

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

Comparison analysis of Bangla news articles classification using support vector machine and logistic regression

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

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