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Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes ge...

Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish.

BMC medical informatics and decision making
BACKGROUND: Controlled vocabularies are fundamental resources for information extraction from clinical texts using natural language processing (NLP). Standard language resources available in the healthcare domain such as the UMLS metathesaurus or SNO...

Vision-Language-Knowledge Co-Embedding for Visual Commonsense Reasoning.

Sensors (Basel, Switzerland)
Visual commonsense reasoning is an intelligent task performed to decide the most appropriate answer to a question while providing the rationale or reason for the answer when an image, a natural language question, and candidate responses are given. Fo...

IrGO: Iranian traditional medicine General Ontology and knowledge base.

Journal of biomedical semantics
BACKGROUND: Iranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified language system has been p...

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models.

Sensors (Basel, Switzerland)
Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively captur...

Pseudotext Injection and Advance Filtering of Low-Resource Corpus for Neural Machine Translation.

Computational intelligence and neuroscience
Scaling natural language processing (NLP) to low-resourced languages to improve machine translation (MT) performance remains enigmatic. This research contributes to the domain on a low-resource English-Twi translation based on filtered synthetic-para...

What Can Network Science Tell Us About Phonology and Language Processing?

Topics in cognitive science
Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of language but neglect many issues related to the representation of language-related information in t...

Improving Loanword Identification in Low-Resource Language with Data Augmentation and Multiple Feature Fusion.

Computational intelligence and neuroscience
Loanword identification is studied in recent years to alleviate data sparseness in several natural language processing (NLP) tasks, such as machine translation, cross-lingual information retrieval, and so on. However, recent studies on this topic usu...

Deep joint learning for language recognition.

Neural networks : the official journal of the International Neural Network Society
Deep learning methods for language recognition have achieved promising performance. However, most of the studies focus on frameworks for single types of acoustic features and single tasks. In this paper, we propose the deep joint learning strategies ...