AIMC Topic: Semantics

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Relation classification via BERT with piecewise convolution and focal loss.

PloS one
Recent relation extraction models' architecture are evolved from the shallow neural networks to natural language model, such as convolutional neural networks or recurrent neural networks to Bert. However, these methods did not consider the semantic i...

Dual coding of knowledge in the human brain.

Trends in cognitive sciences
How does the human brain code knowledge about the world? While disciplines such as artificial intelligence represent world knowledge based on human language, neurocognitive models of knowledge have been dominated by sensory embodiment, in which knowl...

Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems.

Computational intelligence and neuroscience
Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in ...

Efficient, high-performance semantic segmentation using multi-scale feature extraction.

PloS one
The success of deep learning in recent years has arguably been driven by the availability of large datasets for training powerful predictive algorithms. In medical applications however, the sensitive nature of the data limits the collection and excha...

The Growing Role for Semantic Segmentation in Urology.

European urology focus
As the quantity and quality of cross-sectional imaging data increase, it is important to be able to make efficient use of the information. Semantic segmentation is an emerging technology that promises to improve the speed, reproducibility, and accura...

Unveiling functions of the visual cortex using task-specific deep neural networks.

PLoS computational biology
The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We r...

Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets.

Journal of biomedical informatics
OBJECTIVES: Biomedical natural language processing tools are increasingly being applied for broad-coverage information extraction-extracting medical information of all types in a scientific document or a clinical note. In such broad-coverage settings...

Visual-guided attentive attributes embedding for zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
Zero-shot learning (ZSL) aims to learn a classifier for unseen classes by exploiting both training data from seen classes and external knowledge. In many visual tasks such as image classification, a set of high-level attributes that describe the sema...

Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation.

Sensors (Basel, Switzerland)
Recent advances in deep learning models for image interpretation finally made it possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large dataset...

A riddle, wrapped in a mystery, inside an enigma: How semantic black boxes and opaque artificial intelligence confuse medical decision-making.

Bioethics
The use of artificial intelligence (AI) in healthcare comes with opportunities but also numerous challenges. A specific challenge that remains underexplored is the lack of clear and distinct definitions of the concepts used in and/or produced by thes...