Classifying Syntactic Errors in Learner Language
| Autor: | Dmitry Nikolaev, Yevgeni Berzak, Omri Abend, Leshem Choshen |
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| Rok vydání: | 2020 |
| Předmět: |
Structure (mathematical logic)
Scheme (programming language) FOS: Computer and information sciences Computer Science - Computation and Language business.industry Computer science computer.software_genre Grammatical error Classification methods Artificial intelligence Representation (mathematics) business computer Computation and Language (cs.CL) Natural language processing Sentence computer.programming_language Universal dependencies |
| Zdroj: | CoNLL |
| DOI: | 10.48550/arxiv.2010.11032 |
| Popis: | We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems. Unlike existing error classification methods, our method is applicable across languages, which we showcase by producing a detailed picture of syntactic errors in learner English and learner Russian. We further demonstrate the utility of the methodology for analyzing the outputs of leading Grammatical Error Correction (GEC) systems. Comment: CoNLL 2020 |
| Databáze: | OpenAIRE |
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