Results 61 to 70 of about 8,049,290 (285)

Incorporating Domain Knowledge into Natural Language Inference on Clinical Texts

IEEE Access, 2019
Making inference on clinical texts is a task which has not been fully studied. With the newly released, expert annotated MedNLI dataset, this task is being boosted. Compared with open domain data, clinical texts present unique linguistic phenomena, e.g.,
Mingming Lu, Yu Fang, Fengqi Yan, Maozhen Li   +3 more
doaj   +1 more source

Evaluation of Digital Technologies for Home‐Based Assessment in People With Amyotrophic Lateral Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Digital technologies hold promise for transforming healthcare by enhancing personalized treatments and offer valuable opportunities to improve patient care. Here, we evaluated several novel, self‐administered, home‐based, digital endpoints for their association with corresponding conventional standard clinical measures (primary) in ...
Arne Mueller, Vanessa Vallejo, Mónica Povedano Panadés, Orla Hardiman, Véronique Danel‐Brunaud, Senda Ajroud‐Driss, Philippe Couratier, Jeremy Shefner, Anna Gokey, Mike DiCesare, Erin Lennox, Jens Praestgaard, Lucie Brujin, Peggy Allred, Ram Miller   +14 more
wiley   +1 more source

Electroencephalographic Normalization as a Biomarker of Clinical Recovery in Down Syndrome Regression Disorder

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Down syndrome regression disorder is a syndrome characterized by subacute loss of cognitive, behavioral, and functional abilities in individuals with Down syndrome. Electroencephalography abnormalities are frequently observed during evaluation, but it remains unclear whether these findings represent a dynamic marker of disease ...
Jonathan D. Santoro, Mackenzie Silverman, Maeve C. Lucas, Mariam M. Yousuf, Samuel T. Otey, Stella V. Gray, Brittany Jordan, Madeline D. Kahan, Latanya D. Agurs, Michelle Van Hirtum Das, Deborah Holder, Devin King, Eileen A. Quinn, Ryan Kammeyer, Michael S. Rafii   +14 more
wiley   +1 more source

Label-aware debiased causal reasoning for Natural Language Inference

AI Open
Recently, researchers have argued that the impressive performance of Natural Language Inference (NLI) models is highly due to the spurious correlations existing in training data, which makes models vulnerable and poorly generalized.
Kun Zhang, Dacao Zhang, Le Wu, Richang Hong, Ye Zhao, Meng Wang   +5 more
doaj   +1 more source

University Student Dropout Prediction Using Pretrained Language Models

Applied Sciences, 2023
Predicting student dropout from universities is an imperative but challenging task. Numerous data-driven approaches that utilize both student demographic information (e.g., gender, nationality, and high school graduation year) and academic information (e.
Hyun-Sik Won, Min-Ji Kim, Dohyun Kim, Hee-Soo Kim, Kang-Min Kim   +4 more
doaj   +1 more source

Endothelial Cell Proteins as Biomarkers in Susac Syndrome

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Susac syndrome (SS) is a rare CD8+ T cell–mediated microangiopathy affecting the brain, retina, and auditory labyrinth. Endothelial injury is thought to be a central mechanism; however, no circulating disease biomarkers are known. We performed targeted proteomic profiling to identify circulating endothelial‐associated proteins as ...
Rohit Benjamin, Yue Andy Qi, Ying Hao, Catherine DeMarino, Samuel Darko, Irene Cortese, Daniel S. Reich, María I. Gaitán, Steven Jacobson, Bridgett J. Billioux, Bryan Smith, Avindra Nath   +11 more
wiley   +1 more source

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, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Evaluating Neural Networks’ Ability to Generalize against Adversarial Attacks in Cross-Lingual Settings

Applied Sciences
Cross-lingual transfer learning using multilingual models has shown promise for improving performance on natural language processing tasks with limited training data.
Vidhu Mathur, Tanvi Dadu, Swati Aggarwal
doaj   +1 more source

Generating Natural Language Inference Chains

CoRR, 2016
The ability to reason with natural language is a fundamental prerequisite for many NLP tasks such as information extraction, machine translation and question answering. To quantify this ability, systems are commonly tested whether they can recognize textual entailment, i.e., whether one sentence can be inferred from another one.
Vladyslav Kolesnyk, Tim Rocktäschel, Sebastian Riedel 0001   +2 more
openaire   +3 more sources

An Exploration of Dropout with RNNs for Natural Language Inference [PDF]

, 2018
Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In this paper, we propose a novel RNN model for NLI and empirically evaluate the effect of applying dropout at
Amit Gajbhiye, Sardar F. Jaf, Noura Al Moubayed, A. Stephen McGough, Steven Bradley   +4 more
openaire   +5 more sources