Results 41 to 50 of about 9,958 (247)
Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction [PDF]
Proceedings of the 55th Annual Meeting of the Association for
Computational Linguistics (Volume 1: Long Papers), 2017 Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them ...
Christopher Bryant 0001 +2 more
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Research progress on Chinese and English text error correction [PDF]
ITM Web of ConferencesText error correction is an essential task in natural language processing (NLP) that focuses on automatically identifying and correcting errors in written text. With the increasing amount of digital text in both Chinese and English, errors such as typos,
Wang Yao
doaj +1 more source
Czech Grammar Error Correction with a Large and Diverse Corpus
Transactions of the Association for Computational Linguistics, 2022 We introduce a large and diverse Czech corpus annotated for grammatical error correction (GEC) with the aim to contribute to the still scarce data resources in this domain for languages other than English.
Jakub Náplava +3 more
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Erroneous data generation for Grammatical Error Correction [PDF]
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, 2019 It has been demonstrated that the utilization of a monolingual corpus in neural Grammatical Error Correction (GEC) systems can significantly improve the system performance. The previous state-of-the-art neural GEC system is an ensemble of four Transformer models pretrained on a large amount of Wikipedia Edits. The Singsound GEC system follows a similar
Shuyao Xu +3 more
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A Two-Stage Model for Chinese Grammatical Error Correction
IEEE Access, 2019 Chinese grammatical error correction (GEC) is more challenging than English GEC due to its language characteristics. In this paper, a two-stage model was proposed to solve the Chinese GEC problem.
Zhaoquan Qiu, Youli Qu
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Grammatical Error Correction in Low-Resource Scenarios [PDF]
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), 2019 Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech.
Náplava, Jakub, Straka, Milan
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A Program Based on Integration between the Error Analysis Approach and the Technical Approach in Treating Grammatical Errors and Improving Self-Efficacy at No Arabic Speaking Learners [PDF]
مجلة بحوث التعليم والإبتكارThe current dissertation attempts to illustrate how effective is a correction program based on the integration of error analysis and technical approaches while emending common grammatical errors and improving self-efficacy among non-native speakers of ...
Heba Madbolli Zeinhom Mohamed +2 more
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The effect of written corrective feedback on grammatical accuracy of EFL students: An improvement over previous unfocused designs [PDF]
Iranian Journal of Language Teaching Research, 2016 The effectiveness of written corrective feedback (WCF) in the improvement of language learners' grammatical accuracy has been a topic of interest in SLA studies for the past couple of decades.
Mobin Khanlarzadeh, Majid Nemati
doaj
Grammatical error correction for Spanish health records
Proces. del Leng. Natural, 2021 This work has been supported by Vicomtech and partially funded by the projects DeepText (KK-2020-00088, SPRI, Basque Government) and DeepReading (RTI2018-096846-B-C21, MCIU/AEI/FEDER, UE).
Lima López, Salvador +2 more
openaire +5 more sources
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source