Building a learner corpus
The paper describes a corpus of texts produced by non-native speakers of Czech. We discuss its annotation scheme, consisting of three interlinked levels to cope with a wide range of error types present in the input. Each level corrects different types of errors; links between the levels allow capturing errors in word order and complex discontinuous expressions. Errors are not only corrected, but also classified. The annotation scheme is tested on a doubly-annotated sample of approx. 10,000 words with fair inter-annotator agreement results. We also explore options of application of automated linguistic annotation tools (taggers, spell checkers and grammar checkers) on the learner text to support or even substitute manual annotation.
Code (0)
등록된 구현이 없습니다.
Tasks
Language AcquisitionSimilar Papers 제목 키워드 기반
Semi-automatically Annotated Learner Corpus for Russian
We present ReLCo— the Revita Learner Corpus—a new semi-automatically annotated learner corpus for Russian. The corpus was collected while several thousand L2 learners were performing exercises using the Revita language-l…
Grammatical Error CorrectionGrammatical Error DetectionBuilding a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English
Building a learner corpus for Russian
Building a TOCFL Learner Corpus for Chinese Grammatical Error Diagnosis
From Labels to Facets: Building a Taxonomically Enriched Turkish Learner Corpus
In terms of annotation structure, most learner corpora rely on holistic flat label inventories which, even when extensive, do not explicitly separate multiple linguistic dimensions. This makes linguistically deep annotat…