The use of offensive language in user-generated content is a serious problem that needs to be addressed with the latest technology. The field of Natural Language Processing (NLP) can support the automatic detection of offensive language. In this survey, we review previous NLP studies that cover Arabic offensive language detection. This survey investigates the state-of-the-art in offensive language detection for the Arabic language, providing a structured overview of previous approaches, including core techniques, tools, resources, methods, and main features used. This work also discusses the limitations and gaps of the previous studies. Findings from this survey emphasize the importance of investing further effort in detecting Arabic offensive language, including the development of benchmark resources and the invention of novel preprocessing and feature extraction techniques.

This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations. To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated. The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83.

No abstract

Preprocessing of input text can play a key role in text classification by reducing dimensionality and removing unnecessary content. This study aims to investigate the impact of preprocessing on Arabic offensive language classification. We explore six preprocessing techniques: conversion of emojis to Arabic textual labels, normalization of different forms of Arabic letters, normalization of selected nouns from dialectal Arabic to Modern Standard Arabic, conversion of selected hyponyms to hypernyms, hashtag segmentation, and basic cleaning such as removing numbers, kashidas, diacritics, and HTML tags. We also experiment with raw text and a combination of all six preprocessing techniques. We apply different types of classifiers in our experiments including traditional machine learning, ensemble machine learning, Artificial Neural Networks, and Bidirectional Encoder Representations from Transformers (BERT)-based models to analyze the impact of preprocessing. Our results demonstrate significant variations in the effects of preprocessing on each classifier type and on each dataset. Classifiers that are based on BERT do not benefit from preprocessing, while traditional machine learning classifiers do. However, these results can benefit from validation on larger datasets that cover broader domains and dialects.

AimThe objective of this study was to investigate the association between the caries experience and oral-health-related behavior of Kuwaiti preschool children and their mothers.Materials and methodsA convenience sample of 84 participants (42 child–mother pairs) was selected. Data regarding children's and mothers’ demographics, oral hygiene practices, and dietary habits were obtained by questionnaires. Oral clinical examinations were carried out on the participant children and mothers to determine their caries experience (decayed, missing, and filled teeth index-dmft/DMFT).ResultsAn estimated 19% of children were caries-free and 66% of mothers have untreated caries. The mean dmft index of the preschool children was 3.90 ± 2.9, and the mean DMFT index of their mothers was 12.38 ± 5.4. Mothers’ untreated caries was significantly associated with their children's untreated caries (r = 0.183, p < 0.05). No correlation was found between the brushing frequencies of children and their mothers (p = 0.582). High consumption of sugary snacks and sugary beverages was detected among the children and mothers with a significant association (p < 0.05). The mean dmft of the children was found to be significantly lower among the young mothers, less than 30 years, (2.4 ± 2.1) compared to that among the mothers older than 30 years (4.3 ± 2.9, p < 0.05).ConclusionThere was a high prevalence of early childhood caries in the preschool children studied. A positive correlation was found between the dental caries experience and sugar consumption of the Kuwaiti preschool children and those of their mothers.Clinical significanceThe oral health status and dietary habits of mothers are potentially significant risk factors for the development of early childhood caries in their children. Pediatric dentists need to identify the main caries risk factors in their community in order to implement appropriate preventive dental care and educational programs.How to cite this articleHusain FAAM, Alanzi AN. Caries Experience and Oral Health-related Factors of Kuwaiti Preschool Children and their Mothers: A Pilot Study. Int J Clin Pediatr Dent 2019;12(4):283–287.