Results 71 to 80 of about 1,159,266 (295)
Arthritis Care &Research, Volume 78, Issue 10, Page 1467-1477, October 2026.Objective To support high‐quality, patient‐centered care for systemic lupus erythematosus (SLE), the American College of Rheumatology (ACR) developed evidence‐based measures incorporating clinical and patient‐reported outcome measures (PROMs). Using the Consolidated Framework for Implementation Research (CFIR), we conducted semistructured interviews ...
Catherine Nasrallah +13 more
wiley +1 more source
Bayesian Neural Word Embedding
, 2017 Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-Gram with negative sampling, known also as word2vec, advanced the state-of-the-art of various linguistics tasks. In
Barkan, Oren
core +1 more source
Arthritis Care &Research, EarlyView.Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah +4 more
wiley +1 more source
Toward Word Embedding for Personalized Information Retrieval
, 2016 International audienceThis paper presents preliminary works on using Word Embedding (word2vec) for query expansion in the context of Personalized Information Retrieval. Traditionally, word em-beddings are learned on a general corpus, like Wikipedia.
Géry, Mathias +2 more
core +4 more sources
Biomedical Word Sense Disambiguation with Word Embeddings [PDF]
, 2017 There is a growing need for automatic extraction of information and knowledge from the increasing amount of biomedical and clinical data produced, namely in textual form. Natural language processing comes in this direction, helping in tasks such as information extraction and information retrieval.
Rui Antunes 0002, Sérgio Matos
openaire +3 more sources
What Do Large Language Models Know About Materials?
Advanced Engineering Materials, EarlyView.If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source
Angular-Based Word Meta-Embedding Learning [PDF]
, 2018 Ensembling word embeddings to improve distributed word representations has shown good success for natural language processing tasks in recent years. These approaches either carry out straightforward mathematical operations over a set of vectors or use ...
Neill, James O', Bollegala, Danushka
core +3 more sources
A Novel Hybrid Deep Learning Model for Sentiment Classification
IEEE Access, 2020 A massive use of social media platforms such as Twitter and Facebook by omnifarious organizations has increased the critical individual feedback on the situation, events, products, and services.
Mehmet Umut Salur, Ilhan Aydin
doaj +1 more source
Jurnal Ilmu Komputer dan Informasi, 2022 Sentiment analysis regarding the COVID-19 vaccine can be obtained from social media because users usually express their opinions through social media.
Kartikasari Kusuma Agustiningsih +2 more
doaj +1 more source
Deconstructing Word Embeddings
CoRR, 2019 A review of Word Embedding Models through a deconstructive approach reveals their several shortcomings and inconsistencies. These include instability of the vector representations, a distorted analogical reasoning, geometric incompatibility with linguistic features, and the inconsistencies in the corpus data.
openaire +3 more sources