Morphosyntactic Analysis for CHILDES
- Houjun Liu, Computer Science, Stanford University (opens in new tab)
- Brian MacWhinney, macw@andrew.cmu.edu(compose email, opens in email app.), Psychology, Carnegie Mellon University (opens in new tab)
Abstract
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. Fortunately, 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 new data for the CHILDES database and have applied the UD (Universal Dependencies) framework to existing data to provide a consistent and comparable morphosyntactic analysis for 27 languages. These new resources open possibilities for deeper crosslinguistic study of language learning.
Keywords:
- morphology
- grammatical relations
- ASR
- NLP
Published on
21 August 2024
Peer Reviewed