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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 continue to 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 des matières
Acknowledgments.- Introduction.- Background.- Special Problems of Language Learners.- Evaluating Error Detection Systems.- Data-Driven Approaches to Articles and Prepositions.- Collocation Errors.- Different Errors and Different Approaches.- Annotating Learner Errors.- Emerging Directions.- Conclusion.- Bibliography.- Authors' Biographies .
A propos de l'auteur
Claudia Leacock is a Research Scientist III at CTB McGraw-Hill where she works on methods for automated scoring. Previously, as a consultant with the Butler Hill Group, she collaborated with the Microsoft Research team that developed ESL Assistant, a web-based prototype tool for detecting and correcting grammatical errors of English language learners. As a Distinguished Member of Technical Staff at Pearson Knowledge Technologies (2004-2007), and previously as a Principal Development Scientist at Educational Testing Service (1997-2004), she developed tools for both automated assessment of short-answer content-based questions and grammatical error detection and correction. As a member of the WordNet group at Princeton University's Cognitive Science Lab (1991-1997), her research focused on word sense disambiguation. Dr. Leacock received a B.A. in English from NYU, a Ph.D. in linguistics from the City University of New York, Graduate Center, and was a post-doctoral fellow at IBM, T.J. Watson Research Center.
