News Classification using Neural Networks

Kaur, Gurmeet; Bajaj, Karan

doi:10.5120/cae2016652224

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Сопоставимые результаты получили G. Kaur с соавторами [2] при классификации англоязычных статей BBC без учёта предметной области на 4 категории: «спорт», «здоровье», «бизнес», «развлечения». Перед классификацией авторы предобрабатывали тексты следующим образом: в текст статей добавляли их заголовки, разбивали тексты на отдельные слова, удаляли стоп-слова, добавляли в текст синонимы слов и стеммировали слова.…”

Section: обзор связанных работunclassified

Classification of Articles from Mass Media by Categories and Relevance of the Subject Area

Larionov

Paramonov

2022

Model. anal. inf. sist.

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The research is devoted to classification of news articles about P. G. Demidov Yaroslavl State University (YarSU) into 4 categories: “society”, “education”, “science and technologies”, “not relevant”.The proposed approaches are based on using the BERT neural network and methods of machine learning: SVM, Logistic Regression, K-Neighbors, Random Forest, in combination of different embedding types: Word2Vec, FastText, TF-IDF, GPT-3. Also approaches of text preprocessing are considered to achieve higher quality of the classification. The experiments showed that the SVM classifier with TF-IDF embedding and trained on full article texts with titles achieved the best result. Its micro-F-measure and macro-F-measure are 0.8214 and 0.8308 respectively. The BERT neural network trained on fragments of paragraphs with YarSU mentions, from which the first 128 words and the last 384 words were taken, showed comparable results. The resulting micro-F-measure and macro-F-measure are 0.8304 and 0.8181 respectively. Thus, using paragraphs with the target organisation mentions is enough to classify text by categories efficiently.

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Section: обзор связанных работunclassified

Classification of Articles from Mass Media by Categories and Relevance of the Subject Area

Larionov

Paramonov

2022

Model. anal. inf. sist.

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“…One of the widespread tasks that are solved using neural networks is classification [2] and clustering [3]. The solution to any classification problem is a bijective relationship, "exampleclass."…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Training Criterion Formulation Using Error Continuous Domain

Hu¹,

Ivashchenko²,

Lyushenko³

et al. 2021

IJMECS

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One of the trends in information technologies is implementing neural networks in modern software packages [1]. The fact that neural networks cannot be directly programmed (but trained) is their distinctive feature. In this regard, the urgent task is to ensure sufficient speed and quality of neural network training procedures. The process of neural network training can differ significantly depending on the problem. There are verification methods that correspond to the task's constraints; they are used to assess the training results. Verification methods provide an estimate of the entire cardinal set of examples but do not allow to estimate which subset of those causes a significant error. This fact leads to neural networks' failure to perform with the given set of hyperparameters, making training a new one time-consuming.On the other hand, existing empirical assessment methods of neural networks training use discrete sets of examples. With this approach, it is impossible to say that the network is suitable for classification on the whole cardinal set of examples.This paper proposes a criterion for assessing the quality of classification results. The criterion is formed by describing the training states of the neural network. Each state is specified by the correspondence of the set of errors to the function range representing a cardinal set of test examples. The criterion usage allows tracking the network's classification defects and marking them as safe or unsafe. As a result, it is possible to formally assess how the training and validation data sets must be altered to improve the network's performance, while existing verification methods do not provide any information on which part of the dataset causes the network to underperform.

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“…With the increase in amount of data stored text categorization became one of the important tasks now-a-days for web searching, datamining, spam filtering, sentiment analysis, decision making etc [2] [18]. Document or text classification is the main task in text mining, natural language processing(NLP) to organize the information in a supervised manner into some categories [16] [6].…”

Section: Introductionmentioning

confidence: 99%

Text classification using artificial neural networks

Prasanna¹,

Rao²

2017

IJET

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Text Categorization is the process of classifying the text or documents into its corresponding categories which are defined previously. As the text or data is increasing enormously now-a-days it's not possible to classify all the text documents manually hence its necessary to use some techniques or methods to classify the text automatically. In this paper we are using the ANN technique to classify the text, the purpose of choosing it, its advantages and its process is described in the further sections.

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News Classification using Neural Networks

Cited by 7 publications

References 12 publications

Classification of Articles from Mass Media by Categories and Relevance of the Subject Area

Classification of Articles from Mass Media by Categories and Relevance of the Subject Area

Artificial Neural Network Training Criterion Formulation Using Error Continuous Domain

Text classification using artificial neural networks

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