Results 61 to 70 of about 2,197,203 (280)

Grammatical Error Correction by Transferring Learning Based on Pre-Trained Language Model

Shanghai Jiaotong Daxue xuebao, 2022
Grammatical error correction (GEC) is a low-resource task, which requires annotations with high costs and is time consuming in training. In this paper, the MASS-GEC is proposed to solve this problem by transferring learning from a pre-trained language ...
HAN Mingyue, WANG Yinglin
doaj   +1 more source

Estimating the prevalence and incidence of symptomatic hand OA in the Netherlands using primary care electronic health records.

Arthritis Care &Research, Accepted Article.
Objective This study estimated the incidence and prevalence of symptomatic hand osteoarthritis (OA) in the Dutch population using a validated algorithm that integrates free‐text data from primary care electronic health records (EHRs). Methods This retrospective cohort study used the Integrated Primary Care Information (IPCI) database, including EHRs ...
Onur Kaya, Jacoline van den Driest, Laura Struik, Margreet Kloppenburg, Ruud Selles, Daniel Prieto Alhambra, Sita Bierma‐Zeinstra, Dieuwke Schiphof   +7 more
wiley   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Grammatical Error Correction: Machine Translation and Classifiers [PDF]

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016
We focus on two leading state-of-the-art approaches to grammatical error correction – machine learning classification and machine translation. Based on the comparative study of the two learning frameworks and through error analysis of the output of the state-of-the-art systems, we identify key strengths and weaknesses of each of these approaches and ...
Alla Rozovskaya, Dan Roth 0001
openaire   +1 more source

Revisiting Grammatical Error Correction Evaluation and Beyond

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022
Pretraining-based (PT-based) automatic evaluation metrics (e.g., BERTScore and BARTScore) have been widely used in several sentence generation tasks (e.g., machine translation and text summarization) due to their better correlation with human judgments over traditional overlap-based methods.
Peiyuan Gong, Xuebo Liu 0002, Heyan Huang, Min Zhang 0005   +3 more
openaire   +3 more sources

RICE: Reasoning-integrated correction evolution for Chinese grammatical error correction

Journal of King Saud University: Computer and Information Sciences
Chinese grammatical error correction remains challenging due to flexible word order, implicit morphology, and the need for context-dependent reasoning.
Yue Yang, Yongbin Qin, Ruizhang Huang, Ruina Bai   +3 more
doaj   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns, Lateef Abdul, Stephan Barth, Martin Glauer, Manuel Günther, Fabian Neuhaus, Jan‐Ole Perschewski, Thomas Bjarsch, Henrik Abel, Johannes Bosch, Niklas Meyer, Peter Hofmann, Marc Drechsler, Alexander Dyck, Dhanunjaya Kumar Nerella, Marvin Tegeler, Oleg Shchyglo, Ingo Steinbach, Timo Klecker, Henk Birkholz, Ehsan Borzabadi Farahani, Bernard Fedelich, Cetin Haftaoglu, Tarakeshwar Lakshmipathy, Elena Garcia Trelles, Christoph Schweizer, Reza Darvishi Kamachali, Lukas Koschmieder, Janin Eiken, Markus Apel, Sung‐Min Wi, Jiangdong Zhao, Andreas Fezer, Nicolas Christ, Patrick Schneider, Eldor Urinov, Veit Königer, Volker Knoblauch, Janik Harter, Thomas Seifert, Thitichai Janpheng, Florian Fuchs, Fabian Teichert, Jörg Schuster, Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann, Felix Wentzien, Heike Wittek, Benjamin Klie, Alexander Röhrs, Ludger Overmeyer, Achraf Atila, Marcel Sadowski, Leopold Talirz, Jan Janssen, Jesper R. Pedersen, Peter Beck, Tejs Vegge, Ivano E. Castelli, Wolfgang Wenzel, Tilmann Hickel, Jörg Schaarschmidt   +67 more
wiley   +1 more source

MTAGEC: Multi-Task Arabic Grammatical Error Correction as a sequence generation with synthetic data

Journal of King Saud University: Computer and Information Sciences
Automatic grammatical error correction (GEC) is challenging due to limited annotated data and complex linguistic features, specifically in Arabic. Existing GEC systems focus on detecting and correcting grammatical errors, overlooking the integration of ...
Zeinab Mahmoud, Marco Zappatore, Natalia Kryvinska, Mohammed Abdalsalam, Ali Alfatemi, Aiman Solyman   +5 more
doaj   +1 more source

Grammar Correction for Multiple Errors in Chinese Based on Prompt Templates

Applied Sciences, 2023
Grammar error correction (GEC) is a crucial task in the field of Natural Language Processing (NLP). Its objective is to automatically detect and rectify grammatical mistakes in sentences, which possesses immense application research value.
Zhici Wang, Qiancheng Yu, Jinyun Wang, Zhiyong Hu, Aoqiang Wang   +4 more
doaj   +1 more source