Error annotation in the COPLE2 corpus

• Autor(a) principal: del Rio, Iria
• Data de Publicação: 2018
• Outros Autores: Mendes, Amália
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://doi.org/10.26334/2183-9077/rapln4ano2018a42
• topic: corpus de aprendentes; anotação do erro; processamento de língua natural; aquisição de L2; learner corpus; error annotation; L2 acquisition; natural language processing

We present the general architecture of the error annotation system applied to the COPLE2 corpus, a learner corpus of Portuguese implemented on the TEITOK platform. We give a general overview of the corpus and of the TEITOK functionalities and describe how the error annotation is structured in a two-level system: first, a fully manual token-based and coarse-grained annotation is applied and produces a rough classification of the errors in three categories, paired with multi-level information for POS and lemma; second, a multi-word and fine-grained annotation in standoff is then semi-automatically produced based on the first level of annotation. The token-based level has been applied to 47% of the total corpus. We compare our system with other proposals of error annotation, and discuss the fine-grained tag set and the experiments to validate its applicability. An inter-annotator (IAA) experiment was performed on the two stages of our system using Cohen’s kappa and it achieved good results on both levels. We explore the possibilities offered by the tokenlevel error annotation, POS and lemma to automatically generate the fine-grained error tags by applying conversion scripts. The model is planned in such a way as to reduce manual effort and rapidly increase the coverage of the error annotation over the full corpus. As the first learner corpus of Portuguese with error annotation, we expect COPLE2 to support new research in different fields connected with Portuguese as second/foreign language, like Second Language Acquisition/Teaching or Computer Assisted Learning.