Morphosyntactic Analysis for CHILDES

Houjun Liu, Brian MacWhinney
7/17/2024
cs.CL

No confident code implementation found for this paper.

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. 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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

IPA-CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modeling

81% match1 repo202025

Morpheme Induction for Emergent Language

81% match2025

Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models

81% match2026

Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application

80% match2024

Evaluating the Effectiveness of Linguistic Knowledge in Pretrained Language Models: A Case Study of Universal Dependencies

80% match2025

Linguistically Grounded Analysis of Language Models using Shapley Head Values

80% match2024

Cite this paper

@article{liu2024morphosyntactic,
  title  = {Morphosyntactic Analysis for CHILDES},
  author = {Houjun Liu and Brian MacWhinney},
  year   = {2024},
  eprint = {2407.12389},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.12389v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2407.12389.svg)](https://codeofpaper.com/paper/2407.12389)

Discussion