Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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
Correcting ESL errors using phrasal SMT techniques
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Modeling Discriminative Global Inference
ICSC '07 Proceedings of the International Conference on Semantic Computing
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
Proceedings of the ACM SIGKDD Workshop on Human Computation
The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Proceedings of the ACM SIGKDD Workshop on Human Computation
The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Financial incentives and the "performance of crowds"
Proceedings of the ACM SIGKDD Workshop on Human Computation
Using first and second language models to correct preposition errors in second language authoring
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Detection of grammatical errors involving prepositions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Fast, cheap, and creative: evaluating translation quality using Amazon's Mechanical Turk
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Automated Grammatical Error Detection for Language Learners
Automated Grammatical Error Detection for Language Learners
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
Using mostly native data to correct errors in learners' writing: a meta-classifier approach
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Cheap, fast and good enough: automatic speech recognition with non-expert transcription
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Predicting human-targeted translation edit rate via untrained human annotators
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Creating speech and language data with Amazon's Mechanical Turk
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Using Amazon Mechanical Turk for transcription of non-native speech
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Annotating named entities in Twitter data with crowdsourcing
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Using Mechanical Turk to annotate lexicons for less commonly used languages
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Non-expert evaluation of summarization systems is risky
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Amazon Mechanical Turk for subjectivity word sense disambiguation
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Annotating ESL errors: challenges and rewards
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Generating confusion sets for context-sensitive error correction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
The circle of meaning: from translation to paraphrasing and back
The circle of meaning: from translation to paraphrasing and back
Amazon mechanical turk: Gold mine or coal mine?
Computational Linguistics
Grammatical error correction with alternating structure optimization
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automated whole sentence grammar correction using a noisy channel model
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Helping our own: the HOO 2011 pilot shared task
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
HOO 2012: a report on the preposition and determiner error correction shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
A beam-search decoder for grammatical error correction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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The last decade has seen an explosion in the number of people learning English as a second language (ESL). In China alone, it is estimated to be over 300 million (Yang in Engl Today 22, 2006). Even in predominantly English-speaking countries, the proportion of non-native speakers can be very substantial. For example, the US National Center for Educational Statistics reported that nearly 10 % of the students in the US public school population speak a language other than English and have limited English proficiency (National Center for Educational Statistics (NCES) in Public school student counts, staff, and graduate counts by state: school year 2000---2001, 2002). As a result, the last few years have seen a rapid increase in the development of NLP tools to detect and correct grammatical errors so that appropriate feedback can be given to ESL writers, a large and growing segment of the world's population. As a byproduct of this surge in interest, there have been many NLP research papers on the topic, a Synthesis Series book (Leacock et al. in Automated grammatical error detection for language learners. Synthesis lectures on human language technologies. Morgan Claypool, Waterloo 2010), a recurring workshop (Tetreault et al. in Proceedings of the NAACL workshop on innovative use of NLP for building educational applications (BEA), 2012), and a shared task competition (Dale et al. in Proceedings of the seventh workshop on building educational applications using NLP (BEA), pp 54---62, 2012; Dale and Kilgarriff in Proceedings of the European workshop on natural language generation (ENLG), pp 242---249, 2011). Despite this growing body of work, several issues affecting the annotation for and evaluation of ESL error detection systems have received little attention. In this paper, we describe these issues in detail and present our research on alleviating their effects.