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Semantics

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MSRA-Net: multi-channel semantic-aware and residual attention mechanism network for unsupervised 3D image registration.

Physics in medicine and biology
. Convolutional neural network (CNN) is developing rapidly in the field of medical image registration, and the proposed U-Net further improves the precision of registration. However, this method may discard certain important information in the proces...

KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts.

IEEE transactions on visualization and computer graphics
Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVIS, a...

Unsupervised domain adaptive building semantic segmentation network by edge-enhanced contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Unsupervised domain adaptation (UDA) is a weakly supervised learning technique that classifies images in the target domain when the source domain has labeled samples, and the target domain has unlabeled samples. Due to the complexity of imaging condi...

A multibranch and multiscale neural network based on semantic perception for multimodal medical image fusion.

Scientific reports
Medical imaging is indispensable for accurate diagnosis and effective treatment, with modalities like MRI and CT providing diverse yet complementary information. Traditional image fusion methods, while essential in consolidating information from mult...

Semantic-enhanced graph neural network for named entity recognition in ancient Chinese books.

Scientific reports
Named entity recognition (NER) plays a crucial role in the extraction and utilization of knowledge of ancient Chinese books. However, the challenges of ancient Chinese NER not only originate from linguistic features such as the use of single characte...

Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory.

Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)
OBJECTIVE: Verbal communication contains key information for mental health assessment. Researchers have linked psychopathology phenomena to certain counterparts in natural language processing. We characterized subtle impairments in the early stages o...

Textual emotion classification using MPNet and cascading broad learning.

Neural networks : the official journal of the International Neural Network Society
As one of the most important tasks of natural language processing, textual emotion classification (TEC) aims to recognize and detect all emotions contained in texts. However, most existing methods are implemented using deep learning approaches, which...

An unsupervised multi-view contrastive learning framework with attention-based reranking strategy for entity alignment.

Neural networks : the official journal of the International Neural Network Society
Entity alignment is a crucial task in knowledge graphs, aiming to match corresponding entities from different knowledge graphs. Due to the scarcity of pre-aligned entities in real-world scenarios, research focused on unsupervised entity alignment has...

Flexibility in conceptual combinations: A neural network model of gradable adjective modification.

PloS one
Our ability to combine simple constituents into more complex conceptual combinations is a fundamental aspect of cognition. Gradable adjectives (e.g., 'tall' and 'light') are a critical example of this process, as their meanings vary depending on the ...

Using natural language processing to facilitate the harmonisation of mental health questionnaires: a validation study using real-world data.

BMC psychiatry
BACKGROUND: Pooling data from different sources will advance mental health research by providing larger sample sizes and allowing cross-study comparisons; however, the heterogeneity in how variables are measured across studies poses a challenge to th...