Automatic analysis of caregiver input and child production

Abstract

The present study explores the applicability of Natural Language Processing (NLP) techniques to investigate child corpora in Korean. We employ caregiver input and child production data in the CHILDES database, currently the largest and open-access Korean child corpus data, and apply NLP techniques to the data in two ways: automatic Part-of-Speech tagging by adapting a machine learning algorithm, and (semi-)automatic extraction of constructional patterns expressing a transitive event (active transitive and suffixal passive). As the first empirical report on NLP-assisted analysis of Korean child corpora, this study is expected to reveal its advantages and drawbacks, thereby opening the window to furthering corpus-mediated research on child language development in Korean. Implications of this study’s findings will also contribute to research practice regarding developmental studies on Korean through child corpora, ensuring the reproducibility of procedures and results, which is often lacking in previous corpus-based research on child language development in Korean.