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Semantics

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Boundary-Aware Dual Biaffine Model for Sequential Sentence Classification in Biomedical Documents.

IEEE/ACM transactions on computational biology and bioinformatics
Assigning appropriate rhetorical roles, such as "background," "intervention," and "outcome," to sentences in biomedical documents can streamline the process for physicians to locate evidence and resources for medical treatment and decision-making. Wh...

Exploring the categorical nature of colour perception: Insights from artificial networks.

Neural networks : the official journal of the International Neural Network Society
The electromagnetic spectrum of light from a rainbow is a continuous signal, yet we perceive it vividly in several distinct colour categories. The origins and underlying mechanisms of this phenomenon remain partly unexplained. We investigate categori...

Research of multi-label text classification based on label attention and correlation networks.

PloS one
Multi-Label Text Classification (MLTC) is a crucial task in natural language processing. Compared to single-label text classification, MLTC is more challenging due to its vast collection of labels which include extracting local semantic information, ...

Integrating Large Language Model, EEG, and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for inter...

Automated linguistic analysis in youth at clinical high risk for psychosis.

Schizophrenia research
Identifying individuals at clinical high risk for psychosis (CHRP) is crucial for preventing psychosis and improving the prognosis for schizophrenia. Individuals at CHR-P may exhibit mild forms of formal thought disorder (FTD), making it possible to ...

simona: a comprehensive R package for semantic similarity analysis on bio-ontologies.

BMC genomics
BACKGROUND: Bio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between bio...

A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.

Sensors (Basel, Switzerland)
Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasiv...

Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation.

Journal of biomedical informatics
OBJECTIVE: The paper introduces a framework for the evaluation of the encoding of factual scientific knowledge, designed to streamline the manual evaluation process typically conducted by domain experts. Inferring over and extracting information from...

BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroe...

Bridging auditory perception and natural language processing with semantically informed deep neural networks.

Scientific reports
Sound recognition is effortless for humans but poses a significant challenge for artificial hearing systems. Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have recently surpassed traditional machine learning in sound c...