Text Classification Based on Convolutional Neural Networks and Word Embedding for Low-Resource Languages: Tigrinya

Fesseha, Awet; Xiong, Shengwu; Emiru, Eshete Derb; Diallo, Moussa; Dahou, Abdelghani

doi:10.3390/info12020052

Cited by 77 publications

(41 citation statements)

References 26 publications

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“…Basically, the general framework of the proposed model consists of preprocessing, feature extraction, feature selection, and classification stages. These stages are suggested in all models that classify using text analysis [41]. The stages of the proposed methodology are shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…Statistical methods ignore the semantic relationships between words, and they have high computational requirements due to their direct processing of word frequencies. However, they are highly suitable for comparing the performance of classification models [41]. Since the proposed methodology focuses on the performance of different machine learning algorithms, we have chosen the statistical methods described below for our study.…”

Section: Feature Extractionmentioning

confidence: 99%

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A Multiclass Approach to Estimating Software Vulnerability Severity Rating with Statistical and Word Embedding Methods

KEKÜL¹,

Ergen²,

Arslan³

2022

IJCNIS

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The analysis and grading of software vulnerabilities is an important process that is done manually by experts today. For this reason, there are time delays, human errors, and excessive costs involved with the process. The final result of these software vulnerability reports created by experts is the calculation of a severity score and a severity rating. The severity rating is the first and foremost value of the software’s vulnerability. The vulnerabilities that can be exploited are only 20% of the total vulnerabilities. The vast majority of exploitations take place within the first two weeks. It is therefore imperative to determine the severity rating without time delays. Our proposed model uses statistical methods and deep learning-based word embedding methods from natural language processing techniques, and machine learning algorithms that perform multi-class classification. Bag of Words, Term Frequency Inverse Document Frequency and Ngram methods, which are statistical methods, were used for feature extraction. Word2Vec, Doc2Vec and Fasttext algorithms are included in the study for deep learning based Word embedding. In the classification stage, Naive Bayes, Decision Tree, K-Nearest Neighbors, Multi-Layer Perceptron, and Random Forest algorithms that can make multi-class classification were preferred. With this aspect, our model proposes a hybrid method. The database used is open to the public and is the most reliable data set in the field. The results obtained in our study are quite promising. By helping experts in this field, procedures will speed up. In addition, our study is one of the first studies containing the latest version of the data size and scoring systems it covers.

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Section: Methodsmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

A Multiclass Approach to Estimating Software Vulnerability Severity Rating with Statistical and Word Embedding Methods

KEKÜL¹,

Ergen²,

Arslan³

2022

IJCNIS

View full text Add to dashboard Cite

show abstract

“…The authors in [ 12 ] proposed text classification based on CNN and word embedding for low-resource languages (Tigrinya). They used manually annotated data sets of 30,000 Tigrinya news texts from various sources, which are divided into six categories: sport, agriculture, politics, religion, education, and health.…”

Section: Related Workmentioning

confidence: 99%

Automated Amharic News Categorization Using Deep Learning Models

Endalie

Haile

2021

Computational Intelligence and Neuroscience

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For decades, machine learning techniques have been used to process Amharic texts. The potential application of deep learning on Amharic document classification has not been exploited due to a lack of language resources. In this paper, we present a deep learning model for Amharic news document classification. The proposed model uses fastText to generate text vectors to represent semantic meaning of texts and solve the problem of traditional methods. The text vectors matrix is then fed into the embedding layer of a convolutional neural network (CNN), which automatically extracts features. We conduct experiments on a data set with six news categories, and our approach produced a classification accuracy of 93.79%. We compared our method to well-known machine learning algorithms such as support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), XGBoost (XGB), and random forest (RF) and achieved good results.

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“…Similar to other neural networks, CNN operates in the same way the brain's visual cortex recognizes and processes things and learns to classify them [63]. CNN has also been applied to speech recognition [64][65][66] and natural language processing (NLP) [67,68].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

2021

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The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above-mentioned challenges in order to enhance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL techniques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT-based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT-based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state-of-the-art research and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL techniques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT.

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Text Classification Based on Convolutional Neural Networks and Word Embedding for Low-Resource Languages: Tigrinya

Cited by 77 publications

References 26 publications

A Multiclass Approach to Estimating Software Vulnerability Severity Rating with Statistical and Word Embedding Methods

A Multiclass Approach to Estimating Software Vulnerability Severity Rating with Statistical and Word Embedding Methods

Automated Amharic News Categorization Using Deep Learning Models

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

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