AIMC Topic: Language

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AHD2FHIR: A Tool for Mapping of Natural Language Annotations to Fast Healthcare Interoperability Resources - A Technical Case Report.

Studies in health technology and informatics
A significant portion of data in Electronic Health Records is only available as unstructured text, such as surgical or finding reports, clinical notes and discharge summaries. To use this data for secondary purposes, natural language processing (NLP)...

A Cross-Modal and Cross-lingual Study of Iconicity in Language: Insights From Deep Learning.

Cognitive science
The present paper addresses the study of non-arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non-arbitrary phonological patterns across a set of ty...

Performance of Machine Learning Methods to Classify French Medical Publications.

Studies in health technology and informatics
Many medical narratives are read by care professionals in their preferred language. These documents can be produced by organizations, authorities or national publishers. However, they are often hardly findable using the usual query engines based on E...

Clustering Nursing Sentences - Comparing Three Sentence Embedding Methods.

Studies in health technology and informatics
In health sciences, high-quality text embeddings may augment qualitative data analysis of large amounts of text by enabling, e.g., searching and clustering of health information. This study aimed to evaluate three different sentence-level embedding m...

Evaluation of Domain-Specific Word Vectors for Biomedical Word Sense Disambiguation.

Studies in health technology and informatics
Among medical applications of natural language processing (NLP), word sense disambiguation (WSD) estimates alternative meanings from text around homonyms. Recently developed NLP methods include word vectors that combine easy computability with nuance...

An analysis of protein language model embeddings for fold prediction.

Briefings in bioinformatics
The identification of the protein fold class is a challenging problem in structural biology. Recent computational methods for fold prediction leverage deep learning techniques to extract protein fold-representative embeddings mainly using evolutionar...

ProteinBERT: a universal deep-learning model of protein sequence and function.

Bioinformatics (Oxford, England)
SUMMARY: Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for t...

Linguistic Redundancy and its Effects on Younger and Older Adults' Real-Time Comprehension and Memory.

Cognitive science
Redundant modifiers can facilitate referential interpretation by narrowing attention to intended referents. This is intriguing because, on traditional accounts, redundancy should impair comprehension. Little is known, however, about the effects of re...

Deep neural architectures for dialect classification with single frequency filtering and zero-time windowing feature representations.

The Journal of the Acoustical Society of America
The goal of this study is to investigate advanced signal processing approaches [single frequency filtering (SFF) and zero-time windowing (ZTW)] with modern deep neural networks (DNNs) [convolution neural networks (CNNs), temporal convolution neural n...