Numerous linguistic operations have been assigned to cortical brain areas, but the contributions of subcortical structures to human language processing are still being discussed. Using simultaneous EEG recordings directly from deep brain structures and the scalp, we show that the human thalamus systematically reacts to syntactic and semantic parameters of auditorily presented language in a temporally interleaved manner in coordination with cortical regions. In contrast, two key structures of the basal ganglia, the globus pallidus internus and the subthalamic nucleus, were not found to be engaged in these processes. We therefore propose that syntactic and semantic language analysis is primarily realized within cortico-thalamic networks, whereas a cohesive basal ganglia network is not involved in these essential operations of language analysis.
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http://dx.doi.org/10.1016/j.neuron.2008.07.011 | DOI Listing |
Sensors (Basel)
January 2025
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100811, China.
While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs.
View Article and Find Full Text PDFIntroduction: Broca's Aphasia (BA) is a language disorder that causes grammatical errors in the language production skills of patients. Contemporary studies revealed the fact that BA patients also have difficulty in analyzing the meaning of phrases and sentences and comprehending the real meaning of the discourse produced by the speaker. The purpose of this study is to investigate possible effect of syntactic movement by changing the word positions in the sentence with morphological markers in order to produce clauses without changing the meaning on the phrasal comprehension skills of Turkish speaking patients with BA.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Center for Cognitive Interaction Technology (CITEC), Technical Faculty, Bielefeld University, Bielefeld, Germany.
Background: In the field of structured information extraction, there are typically semantic and syntactic constraints on the output of information extraction (IE) systems. These constraints, however, can typically not be guaranteed using standard (fine-tuned) encoder-decoder architectures. This has led to the development of constrained decoding approaches which allow, e.
View Article and Find Full Text PDFData 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 PDFSci 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.
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