Morphosyntactic analysis for CHILDES
H Liu, B MacWhinney - arXiv preprint arXiv:2407.12389, 2024 - arxiv.org
arXiv preprint arXiv:2407.12389, 2024•arxiv.org
Language development researchers are interested in comparing the process of language
learning across languages. Unfortunately, it has been difficult to construct a consistent
quantitative framework for such comparisons. However, recent advances in AI (Artificial
Intelligence) and ML (Machine Learning) are providing new methods for ASR (automatic
speech recognition) and NLP (natural language processing) that can be brought to bear on
this problem. Using the Batchalign2 program (Liu et al., 2023), we have been transcribing …
learning across languages. Unfortunately, it has been difficult to construct a consistent
quantitative framework for such comparisons. However, recent advances in AI (Artificial
Intelligence) and ML (Machine Learning) are providing new methods for ASR (automatic
speech recognition) and NLP (natural language processing) that can be brought to bear on
this problem. Using the Batchalign2 program (Liu et al., 2023), we have been transcribing …
Language development researchers are interested in comparing the process of language learning across languages. Unfortunately, it has been difficult to construct a consistent quantitative framework for such comparisons. However, recent advances in AI (Artificial Intelligence) and ML (Machine Learning) are providing new methods for ASR (automatic speech recognition) and NLP (natural language processing) that can be brought to bear on this problem. Using the Batchalign2 program (Liu et al., 2023), we have been transcribing and linking data for the CHILDES database and have applied the UD (Universal Dependencies) framework to provide a consistent and comparable morphosyntactic analysis for 27 languages. These new resources open possibilities for deeper crosslinguistic study of language learning.
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