Results 81 to 90 of about 44,902,161 (285)

Short text classification using very few words

, 2012
We propose a simple, scalable, and non-parametric approach for short text classification. Leveraging the well studied and scalable Information Retrieval (IR) framework, our approach mimics human labeling process for a piece of short text.
Aixin Sun, Sun, Aixin
core   +1 more source

Neurologic Manifestations of Long COVID Affect Adult Females More Severely Than Males

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To characterize differences in neurologic manifestations of postacute sequelae of SARS‐CoV‐2 infection (Neuro‐PASC) between females and males. Methods Cross‐sectional study of the first consecutive 261 posthospitalization Neuro‐PASC (PNP) and 2068 nonhospitalized Neuro‐PASC (NNP) patients evaluated at the Neuro‐COVID clinic between ...
Hannah Kopinsky, Melissa Lopez, Janet Miller, Millenia Jimenez, Eric M. Liotta, Igor J. Koralnik   +5 more
wiley   +1 more source

Text Classification Based on Feature Fusion of Dual Hypergraph Neural Networks [PDF]

Jisuanji gongcheng
In recent years, Graph Neural Networks (GNNs) have been widely used for text classification tasks. Current models based on GNNs first model the text as a graph and then use GNNs to propagate and aggregate the features of the text graph.
ZHENG Cheng, LI Pengfei
doaj   +1 more source

Using Lexical Knowledge in Text Classification

, 1998
This paper describes several experiments in text classification using WordNet, a rich source of lexical background knowledge available in the public domain.
Text Classification, Stan Matwin, Sam Scott, Background Knowledge   +3 more
core  

Modeling Progressive Brain Volume Losses in Chronic Moderate Severe Traumatic Brain Injury: Extensive, but Attenuating

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Progressive brain atrophy has now been widely observed in the chronic stages of moderate–severe traumatic brain injury (msTBI), but there is only coarse information regarding its prevalence and magnitude, and whether rate of atrophy accelerates or attenuates across time. We examined these questions in a Canadian inception cohort from
Robin E. A. Green, Brenda Colella, Alana Changoor, David J. Mikulis, Adrienne Li, Kadeen Johns, Georges A. Monette   +6 more
wiley   +1 more source

The Association of Amyloid‐β and Tau With Synaptic Dysfunction in Alzheimer's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To clarify the relative contributions of amyloid and tau to synaptic dysfunction in Alzheimer's disease (AD). Methods We included 109 cognitively unimpaired and impaired individuals from the Geneva Memory Center with available CSF synaptic biomarkers (neurogranin, SNAP‐25, GAP‐43). Participants were stratified according to amyloid (A)
Fausto Roveta, Augusto J. Mendes, Valentina Garibotto, Cecilia Boccalini, Aurelien Lathuiliere, Innocenzo Rainero, Elisa Rubino, Henrik Zetterberg, Nicholas J. Ashton, Federica Ribaldi, Giovanni B. Frisoni, for the Alzheimer's Disease Neuroimaging Initiative   +11 more
wiley   +1 more source

Section Recommendation of Online Medical Platform Based on Keyword Expansion with Self-Adaptive-Attention-Prompt-BERT-RCNN Modeling

Applied Sciences
Background: Implementing automatic classification of short texts in online healthcare platforms is crucial to increase the efficiency of their services and improve the user experience.
Tianbao Xie, Yuqi Han, Ganglong Duan, Siyu Yang, Shaoyang Zhang, Yongcheng Shao   +5 more
doaj   +1 more source

Multilabel Text Classification in News Articles Using Long-Term Memory with Word2Vec

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2020
Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic ...
Winda Kurnia Sari, Dian Palupi Rini, Reza Firsandaya Malik, Iman Saladin B. Azhar   +3 more
doaj   +1 more source

Token-Level Graphs for Short Text Classification

The classification of short texts is a common subtask in Information Retrieval (IR). Recent advances in graph machine learning have led to interest in graph-based approaches for low resource scenarios, showing promise in such settings. However, existing methods face limitations such as not accounting for different meanings of the same words or ...
Gregor Donabauer, Udo Kruschwitz
openaire   +2 more sources

A New Vector Representation of Short Texts for Classification

The International Arab Journal of Information Technology, 2019
Short and sparse characteristics and synonyms and homonyms are main obstacles for short-text classification. In recent years, research on short-text classification has focused on expanding short texts but has barely guaranteed the validity of expanded words. This study proposes a new method to weaken these effects without external knowledge.
Yangyang Li, Bo Liu 0062
openaire   +1 more source