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Grammatical Error Detection

Grammatical Error Detection (GED) is the task of detecting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors. Grammatical error detection (GED) is one of the key component in grammatical error correction (GEC) community.

Papers

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Showing 76–100 of 100 papers

TitleStatusHype
The NTNU-YZU System in the AESW Shared Task: Automated Evaluation of Scientific Writing Using a Convolutional Neural Network—0
UM-Checker: A Hybrid System for English Grammatical Error Correction—0
UW-Stanford System Description for AESW 2016 Shared Task on Grammatical Error Detection—0
Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection—0
WriteAhead2: Mining Lexical Grammar Patterns for Assisted Writing—0
Addressing Class Imbalance in Grammatical Error Detection with Evaluation Metric Optimization—0
Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection—0
A Grammar Sparrer for Norwegian—0
A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for Chinese Grammatical Error Detection—0
A Light Rule-based Approach to English Subject-Verb Agreement Errors on the Third Person Singular Forms—0
Approximate Conditional Coverage & Calibration via Neural Model Approximations—0
A Report on the Automatic Evaluation of Scientific Writing Shared Task—0
Artificial Error Generation with Machine Translation and Syntactic Patterns—0
ARWI: Arabic Write and Improve—0
A Sentence Judgment System for Grammatical Error Detection—0
Assessing Grammatical Correctness in Language Learning—0
A Tree Transducer Model for Grammatical Error Correction—0
Attending to Characters in Neural Sequence Labeling Models—0
Automated Evaluation of Scientific Writing: AESW Shared Task Proposal—0
Automated Writing Support Using Deep Linguistic Parsers—0
Automatic Grammatical Error Detection for Chinese based on Conditional Random Field—0
Automatic morphological analysis of learner Hungarian—0
Auxiliary Objectives for Neural Error Detection Models—0
A Warm Start and a Clean Crawled Corpus -- A Recipe for Good Language Models—0
A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models—0
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