A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Scaling Up Context-Sensitive Text Correction
Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
Automatic error detection in the Japanese learners' English spoken data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Detecting errors in English article usage by non-native speakers
Natural Language Engineering
A feedback-augmented method for detecting errors in the writing of learners of English
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A classifier-based approach to preposition and determiner error correction in L2 English
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
The ups and downs of preposition error detection in ESL writing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Native judgments of non-native usage: experiments in preposition error detection
HumanJudge '08 Proceedings of the Workshop on Human Judgements in Computational Linguistics
Detection of grammatical errors involving prepositions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Training paradigms for correcting errors in grammar and usage
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Training paradigms for correcting errors in grammar and usage
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Generating confusion sets for context-sensitive error correction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Algorithm selection and model adaptation for ESL correction tasks
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Creating a manually error-tagged and shallow-parsed learner corpus
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
They can help: using crowdsourcing to improve the evaluation of grammatical error detection systems
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
On Morphological Analysis for Learner Language, Focusing on Russian
Research on Language and Computation
University of Illinois system in HOO text correction shared task
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Measuring contextual fitness using error contexts extracted from the Wikipedia revision history
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
HOO 2012 error recognition and correction shared task: Cambridge University submission report
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
The UI system in the HOO 2012 shared task on error correction
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Developing learner corpus annotation for Korean particle errors
LAW VI '12 Proceedings of the Sixth Linguistic Annotation Workshop
Evaluating and automating the annotation of a learner corpus
Language Resources and Evaluation
Bucking the trend: improved evaluation and annotation practices for ESL error detection systems
Language Resources and Evaluation
| Hi-index | 0.00 |
In this paper, we present a corrected and error-tagged corpus of essays written by non-native speakers of English. The corpus contains 63000 words and includes data by learners of English of nine first language backgrounds. The annotation was performed at the sentence level and involved correcting all errors in the sentence. Error classification includes mistakes in preposition and article usage, errors in grammar, word order, and word choice. We show an analysis of errors in the annotated corpus by error categories and first language backgrounds, as well as inter-annotator agreement on the task. We also describe a computer program that was developed to facilitate and standardize the annotation procedure for the task. The program allows for the annotation of various types of mistakes and was used in the annotation of the corpus.