Abstract
In general, multilingual children have lower vocabulary competence than monolingual children when considering only one language (but more when considering all lan- guages). If sufficient input is provided in all languages, however, the Complementarity Principle (Grosjean 2016) hypothesizes equivalent vocabulary for monolinguals and multilinguals speaking the same language. It is our objective to evaluate the vocabulary of monolingual and bilingual students who have been studying together for several years and should, according to the Complementarity Principle, have a comparable vo- cabulary. For the analysis of student texts, we will use three sets of texts taken from the LEONIDE corpus (Glaznieks et al., 2022). The sets were built to take into ac- count the language background of the students (monolingual German-speakers, bilin- gual German-speakers, and multilingual non-L1-German-speakers). Due to the fact that the texts of the students are not equal, we will utilize the Lexical Diversity Measure to compare the vocabulary of each student. It has been shown that learner character- ristics profoundly influence measures of lexical diversity, range, and sophistication (e.g., Meurers 2015). This poster will try an evaluation of different tools and measures for lexical diversity, including an evaluation of the RNN-tagger developed by Schmidt (2019). Considering school-related topics, we expect that the vocabulary of monolin- gual and bilingual students after a sufficient period of schooling is equally large. More- over, we hope to gain knowledge about which measures of lexical diversity are more useful for (short) student texts. Consequently, lexical diversity measures would likely be useful tools for comparing the vocabulary of individual texts, and the expected re- sults would have important implications for the judgement of the vocabulary of multilin- gual individuals.s dem LEONIDE-Korpus.
References:
Glaznieks, A., Frey, J.-C., Stopfner, M., Zanasi, L. & Nicolas, L. (2022): LEONIDE: A longitudinal trilingual corpus of young learners of Italian, German and English. International Journal of Learner Corpus Research 8:1, 97-120. https://doi.org/10.1075/ijlcr.21004.gla
Grosjean, F. (2016). The Complementarity Principle and its impact on processing, acquisition, and dominance. In J. Treffers-
Hammou, B. A., Larouz, M., & Fagroud, M. (2021). Word frequency, Range and Lexical diversity: Picking out Changes in Lexical Proficiency among University Learners in an EFL Context. International Journal of Linguistics and Translation Studies, 2(2), 22-38.
Kyle, K., Crossley, S. A., & Jarvis, S. (2021). Assessing the Validity of Lexical Diversity Indices Using Direct Judgements. Language Assessment Quarterly, 18(2), 154-170. https://10.1080/15434303.2020.1844205
Meurers, D. (2015). Learner corpora and natural language processing. The Cambridge Handbook of Learner Corpus Research, 537–566.
Schmidt, H. (2019). Deep Learning-Based Morphological Taggers and Lemmatizers for Annotating Historical Texts, DATeCH, May 2019, Brussels, Belgium.