AIMC Topic: Semantics

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Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement.

Journal of biomedical informatics
OBJECTIVE: Ontologies are essential for representing the knowledge of a domain. To make ontologies useful, they must encompass a comprehensive domain view. To achieve ontology enrichment, there is a need to discover new concepts to be added, either b...

sEntIMeldCL: Enhancing explicit knowledge via Uniform-based Implicit Contrastive Mechanism for Aspect-Level Sentiment Analysis.

Neural networks : the official journal of the International Neural Network Society
Aspect-level sentiment analysis (ALSA) is a fine-grained task which consists of aspect terms and sentiment polarities within sentences. Numerous research studies only focus on the syntactical dependencies between words, ignoring the impact of negatio...

A method for feature division of Soccer Foul actions based on salience image semantics.

PloS one
The purpose of this study is to realize the automatic identification and classification of fouls in football matches and improve the overall identification accuracy. Therefore, a Deep Learning-Based Saliency Prediction Model (DLSPM) is proposed. DLSP...

SCATrans: semantic cross-attention transformer for drug-drug interaction predication through multimodal biomedical data.

BMC bioinformatics
Predicting potential drug-drug interactions (DDIs) from biomedical data plays a critical role in drug therapy, drug development, drug regulation, and public health. However, it remains challenging due to the large number of possible drug combinations...

Spatiotemporal dynamics of reading Kana (syllabograms) and Kanji (morphograms).

NeuroImage
Reading engages complex neural networks integrating visual, phonological, and semantic information. The dual-stream model posits ventral and dorsal pathways for lexical and sublexical processing in the left hemisphere and is well-supported in alphabe...

Comparative analysis of semantic-segmentation models for screen film mammograms.

Computers in biology and medicine
Accurate segmentation of mammographic mass is very important as shape characteristics of these masses play a significant role for radiologist to diagnose benign and malignant cases. Recently, various deep learning segmentation algorithms have become ...

Neuro-symbolic procedural semantics for explainable visual dialogue.

PloS one
This paper introduces a novel approach to visual dialogue that is based on neuro-symbolic procedural semantics. The approach builds further on earlier work on procedural semantics for visual question answering and expands it with neuro-symbolic mecha...

Creating, anonymizing and evaluating the first medical language model pre-trained on Dutch Electronic Health Records: MedRoBERTa.nl.

Artificial intelligence in medicine
Electronic Health Records (EHRs) contain written notes by all kinds of medical professionals about all aspects of well-being of a patient. When adequately processed with a Large Language Model (LLM), this enormous source of information can be analyze...

EnsembleEdgeFusion: advancing semantic segmentation in microvascular decompression imaging with innovative ensemble techniques.

Scientific reports
Semantic segmentation involves an imminent part in the investigation of medical images, particularly in the domain of microvascular decompression, where publicly available datasets are scarce, and expert annotation is demanding. In response to this c...

CoupleMDA: Metapath-Induced Structural-Semantic Coupling Network for miRNA-Disease Association Prediction.

International journal of molecular sciences
The prediction of microRNA-disease associations (MDAs) is crucial for understanding disease mechanisms and biomarker discovery. While graph neural networks have emerged as promising tools for MDA prediction, existing methods face critical limitations...