A Comparative Study of Arabic Part of Speech Taggers Using Literary Text Samples from Saudi Novels

Alluhaibi, Reyadh; Alfraidi, Tareq; Abdeen, M.; Yatimi, Ahmed

doi:10.3390/info12120523

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…The choice for ALP was made based on a comparative study of the five most common Arabic POS taggers, the Stanford Arabic tagger ( https://nlp.stanford.edu/software/tagger.shtml ), CAMeL Tools ( https://github.com/CAMeL-Lab/camel_tools ), MADAMIRA ( https://camel.abudhabi.nyu.edu/madamira ), Farasa ( https://farasa.qcri.org/POS ), and ALP ( http://arabicnlp.pro/ ). The study used text samples from Saudi novels and found that the ALP outperformed the other taggers ( Alluhaibi et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Detection of cyberhate speech towards female sport in the Arabic Xsphere

Alhayan,

Almobarak,

Shalabi

et al. 2024

PeerJ Computer Science

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The recent rapid growth in the number of Saudi female athletes and sports enthusiasts’ presence on social media has exposed them to gender-hate speech and discrimination. Hate speech, a harmful worldwide phenomenon, can have severe consequences. Its prevalence in sports has surged alongside the growing influence of social media, with X serving as a prominent platform for the expression of hate speech and discriminatory comments, often targeting women in sports. This research combines two studies that explores online hate speech and gender biases in the context of sports, proposing an automated solution for detecting hate speech targeting women in sports on platforms like X, with a particular focus on Arabic, a challenging domain with limited prior research. In Study 1, semi-structured interviews with 33 Saudi female athletes and sports fans revealed common forms of hate speech, including gender-based derogatory comments, misogyny, and appearance-related discrimination. Building upon the foundations laid by Study 1, Study 2 addresses the pressing need for effective interventions to combat hate speech against women in sports on social media by evaluating machine learning (ML) models for identifying hate speech targeting women in sports in Arabic. A dataset of 7,487 Arabic tweets was collected, annotated, and pre-processed. Term frequency-inverse document frequency (TF-IDF) and part-of-speech (POS) feature extraction techniques were used, and various ML algorithms were trained Random Forest consistently outperformed, achieving accuracy (85% and 84% using TF-IDF and POS, respectively) compared to other methods, demonstrating the effectiveness of both feature sets in identifying Arabic hate speech. The research contribution advances the understanding of online hate targeting Arabic women in sports by identifying various forms of such hate. The systematic creation of a meticulously annotated Arabic hate speech dataset, specifically focused on women’s sports, enhances the dataset’s reliability and provides valuable insights for future research in countering hate speech against women in sports. This dataset forms a strong foundation for developing effective strategies to address online hate within the unique context of women’s sports. The research findings contribute to the ongoing efforts to combat hate speech against women in sports on social media, aligning with the objectives of Saudi Arabia’s Vision 2030 and recognizing the significance of female participation in sports.

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Section: Methodsmentioning

confidence: 99%

Detection of cyberhate speech towards female sport in the Arabic Xsphere

Alhayan,

Almobarak,

Shalabi

et al. 2024

PeerJ Computer Science

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“…In this step, the words of the corpus were annotated with POS tags using the free Arabic Linguistics Pipeline (ALP) tool [59] (The tool can be accessed via the following link: http://arabicnlp.pro/alp/index-ar.php, accessed on 12 April 2022). This tool was chosen objectively based on the results of a study by [60] that compared five well-known Arabic POS taggers (Arabic Stanford, CAMeL, Farasa, MADAMIRA, and ALP) using a sample from the current corpus. ALP performed better along all three considered metrics (Recall, Precision, and F-score).…”

Section: Word Annotationmentioning

confidence: 99%

The Saudi Novel Corpus: Design and Compilation

Alfraidi

Abdeen²,

Yatimi³

et al. 2022

Applied Sciences

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Arabic has recently received significant attention from corpus compilers. This situation has led to the creation of many Arabic corpora that cover various genres, most notably the newswire genre. Yet, Arabic novels, and specifically those authored by Saudi writers, lack the sufficient digital datasets that would enhance corpus linguistic and stylistic studies of these works. Thus, Arabic lags behind English and other European languages in this context. In this paper, we present the Saudi Novels Corpus, built to be a valuable resource for linguistic and stylistic research communities. We specifically present the procedures we followed and the decisions we made in creating the corpus. We describe and clarify the design criteria, data collection methods, process of annotation, and encoding. In addition, we present preliminary results that emerged from the analysis of the corpus content. We consider the work described in this paper as initial steps to bridge the existing gap between corpus linguistics and Arabic literary texts. Further work is planned to improve the quality of the corpus by adding advanced features.

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The Najdi Arabic Corpus: a new corpus for an underrepresented Arabic dialect

Alhedayani

2024

Lang Resources & Evaluation

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A Comparative Study of Arabic Part of Speech Taggers Using Literary Text Samples from Saudi Novels

Cited by 7 publications

References 13 publications

Detection of cyberhate speech towards female sport in the Arabic Xsphere

Detection of cyberhate speech towards female sport in the Arabic Xsphere

The Saudi Novel Corpus: Design and Compilation

The Najdi Arabic Corpus: a new corpus for an underrepresented Arabic dialect

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