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020 _a9783031021534
_9978-3-031-02153-4
024 7 _a10.1007/978-3-031-02153-4
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aLeacock, Claudia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987810
245 1 0 _aAutomated Grammatical Error Detection for Language Learners, Second Edition
_h[electronic resource] /
_cby Claudia Leacock, Michael Gamon, Joel Alejandro Mejia, Martin Chodorow.
250 _a2nd ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXV, 154 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aAcknowledgments -- 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 .
520 _aIt 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
700 1 _aGamon, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987815
700 1 _aMejia, Joel Alejandro.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987817
700 1 _aChodorow, Martin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987819
710 2 _aSpringerLink (Online service)
_987821
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031010255
776 0 8 _iPrinted edition:
_z9783031032813
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_987823
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02153-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c86153
_d86153