LexComSpaL2 : a lexical complexity corpus for Spanish as a foreign language
- Publication type
- C1
- Publication status
- Published
- Authors
- Degraeuwe, JRD, & Goethals, P.
- Editor
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti and Nianwen Xue
- Series
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Pagination
- 10432-10447
- Publisher
- ELRA
- Conference
- 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (Turin, Italy)
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- (.pdf)
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Abstract
We present LexComSpaL2, a novel corpus which can be employed to train personalised word-level difficulty classifiers for learners of Spanish as a foreign/second language (L2). The dataset contains 2,240 in-context target words with the corresponding difficulty judgements of 26 Dutch-speaking students who are learning Spanish as an L2, resulting in a total of 58,240 annotations. The target words are divided over 200 sentences from 4 different domains (economics, health, law, and migration) and have been selected based on their suitability to be included in L2 learning materials. As our annotation scheme, we use a customised version of the 5-point lexical complexity prediction scale (Shardlow et al., 2020), tailored to the vocabulary knowledge continuum (which ranges from no knowledge over receptive mastery to productive mastery; Schmitt, 2019). With LexComSpaL2, we aim to address the lack of relevant data for multi-category difficult prediction at word level for L2 learners of other languages than English.