Abstract
Language acquisition is a scientific puzzle still awaiting a theoretical solution. Children seem to acquire their native language in a spontaneous and effortless way and they probably do so by keeping track of the frequency with which language items such as phonemes or parts of speech occur. Advances in data storage, processing and visualization have triggered a growing and fertile interest in analysing language by relying on statistics and quantitative methods. In this paper we propose a multiple logistic regression model to evaluate how different components of language contribute to its acquisition over time. The empirical basis consists of a corpus, which can be considered as a series of statistically representative samples taken at regular time intervals. The aim is to show how quantitative methods can contribute to explaining the creation and development of grammatical categories in first language acquisition.
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Notes
- 1.
Among the seven children in the CoLaJE database we choose Adrien because, from a sampling point of view, the data is more detailed and complete.
- 2.
Classical linear regression was discarded because it gave poor results during the modelling stage.
- 3.
We transformed the variable from months to years for a better representation of the data.
- 4.
It is possible to calculate the IPC of any given French word at this link [10]: http://igm.univ-mlv.fr/~gambette/iPhocomp/.
- 5.
Universal Dependencies (UD) is a framework for consistent annotation of grammar (parts of speech, morphological features, and syntactic dependencies) across different human languages.
- 6.
Before modelling AGE as linear, we tried to create three successive yearly time slots to see how the two other regressors behave if taken apart, but the resulting classified number of cases had a lower success rate than the model proposed. We then choose to model it as linear because first language acquisition is a highly non-linear phenomenon and the only certainty linguists have is that – roughly speaking – it develops in a cumulative way over time. We tried to model the interaction effects between COMPLEX, IPC and CLASS too, but it turned out to be less precise than the model proposed: in fact, COMPLEX showed a counterintuitive result in which its increase in value causes a decrease in WPV (models are available on request).
- 7.
All calculations are performed with STATA ver. 15.
- 8.
The AGE in the abscissa axis refers to the period of the video recordings (see CoLaJE database [4] for more details).
- 9.
The Sentence Phonetic Variation Rate (SPVR) is the ratio between the number of phonetic variations (the number of differences detected between “pho” and “mod”) and the total numbers of words. SPVR can assume the value 0% when the child does not make any errors and 100% when the child does not correctly pronounce any of the words contained in the sentence [17].
- 10.
To have an idea of what 0 or 4 or 8 mean, you can type a word in the link [10]: http://igm.univ-mlv.fr/~gambette/iPhocomp/.
- 11.
The past participle form in French could be given as an example.
- 12.
A graphic visualization of this work can be found at this link [17]: http://advanse.lirmm.fr/EMClustering/.
- 13.
Considering the low number of "OTHER" type words, only the most important classes were taken: CLOSED and OPEN.
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Briglia, A., Mucciardi, M., Pirrotta, G. (2022). A Statistical Model for Predicting Child Language Acquisition: Unfolding Qualitative Grammatical Development by Using Logistic Regression Model. In: Salvati, N., Perna, C., Marchetti, S., Chambers, R. (eds) Studies in Theoretical and Applied Statistics . SIS 2021. Springer Proceedings in Mathematics & Statistics, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-031-16609-9_7
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