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Morphologically-analyzed and syntactically-annotated Quran dataset.
Data Brief
February 2025
Arabic Department, University of Sharjah, UAE.
This paper introduces the Morphologically-Analyzed and Syntactically-Annotated Quran (MASAQ) dataset, a comprehensive resource designed to address the scarcity of annotated Quranic Arabic corpora and facilitate the development of advanced Natural Language Processing (NLP) models. The Quran, being a cornerstone of classical Arabic, presents unique challenges for NLP due to its sacred nature and complex linguistic features. MASAQ provides a detailed syntactic and morphological annotation of the entire Quranic text, utilizing a rigorously verified text from Tanzil.
View Article and Find Full Text PDFHierarchical graph-based integration network for propaganda detection in textual news articles on social media.
Sci Rep
January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFLow-frequency neural activity tracks syntactic information through semantic mediation.
Brain Lang
January 2025
School of Communication Sciences, Beijing Language and Culture University, Beijing 100083, China.
How our brain integrates single words into larger linguistic units is a central focus in neurolinguistic studies. Previous studies mainly explored this topic at the semantic or syntactic level, with few looking at how cortical activities track word sequences with different levels of semantic correlations. In addition, prior research did not tease apart the semantic factors from the syntactic ones in the word sequences.
View Article and Find Full Text PDFNeural correlates of auditory comprehension and integration of sanskrit verse: a functional MRI study.
Brain Struct Funct
January 2025
CHRIST (Deemed to be University), Bangalore, Karnataka, India.
In this investigation, we delve into the neural underpinnings of auditory processing of Sanskrit verse comprehension, an area not previously explored by neuroscientific research. Our study examines a diverse group of 44 bilingual individuals, including both proficient and non-proficient Sanskrit speakers, to uncover the intricate neural patterns involved in processing verses of this ancient language. Employing an integrated neuroimaging approach that combines functional connectivity-multivariate pattern analysis (fc-MVPA), voxel-based univariate analysis, seed-based connectivity analysis, and the use of sparse fMRI techniques to minimize the interference of scanner noise, we highlight the brain's adaptability and ability to integrate multiple types of information.
View Article and Find Full Text PDFMismatch Negativity Unveils Tone Perception Strategies and Degrees of Tone Merging: The Case of Macau Cantonese.
Brain Sci
December 2024
Faculty of Arts and Humanities, University of Macau, Macau SAR 999078, China.
Background/objectives: Previous studies have examined the role of working memory in cognitive tasks such as syntactic, semantic, and phonological processing, thereby contributing to our understanding of linguistic information management and retrieval. However, the real-time processing of phonological information-particularly in relation to suprasegmental features like tone, where its contour represents a time-varying signal-remains a relatively underexplored area within the framework of Information Processing Theory (IPT). This study aimed to address this gap by investigating the real-time processing of similar tonal information by native Cantonese speakers, thereby providing a deeper understanding of how IPT applies to auditory processing.
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