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Projeto de investigação
DETECÇÃO E CORREÇÃO AUTOMÁTICA DE ERROS EM PORTUGUÊS SEGUNDA LÍNGUA/LÍNGUA ESTRANGEIRA
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Publicações
Error annotation in the COPLE2 corpus
Publication . del Río, Iria; Mendes, Amália
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 token-level 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.
Error annotation in a Learner Corpus of Portuguese
Publication . Mendes, Amália; del Río, Iria
We present the error tagging system of the COPLE2 corpus and the first results of its implementation.. The system takes advantage of the corpus architecture and the possibilities of the TEITOK environment to reduce manual effort and produce a final standoff, multilevel annotation with position-based tags that account for the main error types observed in the corpus. The first step of the tagging process involves the manual annotation of errors at the token level. We have already annotated 47% of the corpus using this approach. In a further step, the token-based annotations will be automatically transformed (fully or partially) in position-based error tags. COPLE2 is the first Portuguese learner corpus with error annotation. We expect that this work will support new research in different fields connected with Portuguese as second/foreign language, like Second Language Acquisition/Teaching or Computer Assisted Learning.
A Portuguese Native Language Identification Dataset
Publication . del Río, Iria; Zampieri, Marcos; Malmasi, Shervin
In this paper we present NLI-PT, the first Portuguese dataset compiled for Native Language Identification (NLI), the task of identifying an author’s first language based on their second language writing. The dataset includes 1,868 student essays written by learners of European Portuguese, native speakers of the following L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. NLI-PT includes the original student text and four different types of annotation: POS, fine-grained POS, constituency parses, and dependency parses. NLI-PT can be used not only in NLI but also in research on several topics in the field of Second Language Acquisition and educational NLP. We discuss possible applications of this dataset and present the results obtained for the first lexical baseline system for Portuguese NLI.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
OE
Número da atribuição
SFRH/BPD/109914/2015