Automated Grammatical Error Detection for Language Learners (Google eBook)
It has been estimated that over a billion people are using or learning English as a second or foreign language, and the numbers are growing not only for English but for other languages as well. These language learners provide a burgeoning market for tools that help identify and correct learners' writing errors. Unfortunately, the errors targeted by typical commercial proofreading tools do not include those aspects of a second language that are hardest to learn. This volume describes the types of constructions English language learners find most difficult -- constructions containing prepositions, articles, and collocations. It provides an overview of the automated approaches that have been developed to identify and correct these and other classes of learner errors in a number of languages. Error annotation and system evaluation are particularly important topics in grammatical error detection because there are no commonly accepted standards. Chapters in the book describe the options available to researchers, recommend best practices for reporting results, and present annotation and evaluation schemes. The final chapters explore recent innovative work that opens new directions for research. It is the authors' hope that this volume will contribute to the growing interest in grammatical error detection by encouraging researchers to take a closer look at the field and its many challenging problems. Table of Contents: Introduction / History of Automated Grammatical Error Detection / Special Problems of Language Learners / Language Learner Data / Evaluating Error Detection Systems / Article and Preposition Errors / Collocation Errors / Different Approaches for Different Errors / Annotating Learner Errors / New Directions / Conclusion
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Different Approaches for Different Errors
Annotating Learner Errors
agreement annotation schemes approach article errors articles and prepositions artiﬁcially beneﬁt bigram Chapter Chodorow 2008b classiﬁer collocation errors comma Computational Linguistics constructions context correct usage counts deﬁnite detection and correction developed difﬁcult English language learners error detection systems error types error-annotated ESL Assistant essays evaluation example false positives feedback ﬁeld ﬁnd ﬁrst ﬂagged frequencies grammar checker grammatical error detection identiﬁed input language model learner corpora Learner Corpus learner data learner errors learner writing lexical machine learning machine translation mal-rules methods Michael Gamon Microsoft million words Error-tagged multiple raters n-gram native speakers Natural Language Processing noun phrase parse trees parser part-of-speech tags performance phrasal verbs pointwise mutual information PoS tag precision and recall problem proﬁciency rules Second Language semantic sentences signiﬁcant speciﬁc spelling errors statistical Stefan Evert suggested syntactic target Tetreault and Chodorow well-formed text word sense disambiguation WordNet writer’s