AIMC Topic: Semantics

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Character-Level Neural Language Modelling in the Clinical Domain.

Studies in health technology and informatics
Word embeddings have become the predominant representation scheme on a token-level for various clinical natural language processing (NLP) tasks. More recently, character-level neural language models, exploiting recurrent neural networks, have again r...

A Semantic Similarity Evaluation for Healthcare Ontologies Matching to HL7 FHIR Resources.

Studies in health technology and informatics
Healthcare 4.0 demands healthcare data to be shaped into a common standardized and interoperable format for achieving more efficient data exchange. Most of the techniques addressing this domain are dealing only with specific cases of data transformat...

Automated Cardiovascular Pathology Assessment Using Semantic Segmentation and Ensemble Learning.

Journal of digital imaging
Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac ci...

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition.

Cognitive science
Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-ste...

Cross-lingual semantic annotation of biomedical literature: experiments in Spanish and English.

Bioinformatics (Oxford, England)
MOTIVATION: Biomedical literature is one of the most relevant sources of information for knowledge mining in the field of Bioinformatics. In spite of English being the most widely addressed language in the field; in recent years, there has been a gro...

Multi-scale structural analysis of proteins by deep semantic segmentation.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidat...

Graph embedding on biomedical networks: methods, applications and evaluations.

Bioinformatics (Oxford, England)
MOTIVATION: Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks ...

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain.

Database : the journal of biological databases and curation
The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where seman...

Gene Ontology Semantic Similarity Analysis Using GOSemSim.

Methods in molecular biology (Clifton, N.J.)
The GOSemSim package, an R-based tool within the Bioconductor project, offers several methods based on information content and graph structure for measuring semantic similarity among GO terms, gene products and gene clusters. In this chapter, I illus...

The influence of place and time on lexical behavior: A distributional analysis.

Behavior research methods
We measured and documented the influence of corpus effects on lexical behavior. Specifically, we used a corpus of over 26,000 fiction books to show that computational models of language trained on samples of language (i.e., subcorpora) representative...