Kurdish News Dataset Headlines (KNDH) through multiclass classification

Badawi, Soran; Saeed, Ari M.; Ahmed, Sara Atito Ali; Abdalla, Peshraw Ahmed; Hassan, Diyari

doi:10.1016/j.dib.2023.109120

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…The second dataset, KNDH, is a massive collection of 50,000 news headlines from popular Kurdish news websites. Each category has an equal number of samples, including social, sport, science, health, and economy (Badawi, et al, 2023). This dataset is freely available to access at https://data.mendeley.com/datasets/kb7vvkg2th/2.…”

Section: A Dataset Collectionmentioning

confidence: 99%

Bridging the Gap

Badawi

2024

ARO

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Effective organization and retrieval of news content are heavily reliant on accurate news classification. While the mountainous research has been conducted in resourceful languages like English and Chinese, the researches on under-resourced languages like the Kurdish language are severely lacking. To address this challenge, we introduce a hybrid approach called RFO-CNN in this paper. The proposed method combines an improved version of red fox optimization algorithm (RFO) and convolutional neural network (CNN) for finetuning CNN’s parameters. Our model’s efficacy was tested on two widely used Kurdish news datasets, KNDH and KDC-4007, both of which contain news articles classified into various categories. We compared the performance of RFO-CNN to other cutting-edge deep learning models such as bidirectional long short-term memory networks and bidirectional encoder representations from transformers (BERT) transformers, as well as classical machine learning approaches such as multinomial naive bayes, support vector machine, and K-nearest neighbors. We trained and tested our datasets using four different scenarios: 60:40, 70:30, 80:20, and 90:10. Our experimental results demonstrate the superiority of the RFO-CNN model across all scenarios, outperforming the benchmark BERT model and other machine learning models in terms of accuracy and F1-score.

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Section: A Dataset Collectionmentioning

confidence: 99%

Bridging the Gap

Badawi

2024

ARO

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“…Although the number of samples is equal for each category, the percentage ratio of the channels used to collect the headlines is different. A total of 34 different channels were utilized to gather the headlines for each category, including 8 channels for economics, 14 for health, 18 for science, 15 for social, and 5 for sport [20] as shown in Figure 2.…”

Section: Datasetsmentioning

confidence: 99%

Data Augmentation For Sorani Kurdish News Headline Classification Using Back-Translation And Deep Learning Model

Badawi

2023

KJAR

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With the increase in the volume of news articles and headlines being generated, it is becoming more difficult for individuals to keep up with the latest developments and find relevant news articles in the Kurdish language. To address this issue, this paper proposes a novel data augmentation approach for improving the performance of Kurdish news headline classification using back-translation and a proposed deep learning Bidirectional Long Short-Term Memory (BiLSTM) model. The approach involves generating synthetic training data by translating Kurdish headlines into a target language in this context English language and back-translating them to the Kurdish language, resulting in an augmented dataset. The proposed BiLSTM model is trained on the augmented data and compared with baseline models SVM (Support-Vector-Machines) and Naïve Bayes an trained on the original data. The experimental results demonstrate that the proposed BiLSTM model outperforms the baseline model and other existing models, achieving state-of-the-art performance on the Kurdish news headline classification task. The findings suggest that the combination of back-translation and a proposed BiLSTM model is a promising approach for data augmentation in low-resource languages, contributing to the advancement of natural language processing in under-resourced languages. Moreover, having a Kurdish news headline classification model can improve access to news and information for Kurdish speakers. With the classification model, they can easily and quickly search for news articles that interest them based on their preferred categories, such as politics, sports, or entertainment.

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