Login / Signup

NSP-SCD: A corpus construction protocol for child-directed print in understudied languages.

Sonali NagSunila JohnAakash Agrawal
Published in: Behavior research methods (2024)
Child-directed print corpora enable systematic psycholinguistic investigations, but this research infrastructure is not available in many understudied languages. Moreover, researchers of understudied languages are dependent on manual tagging because precise automatized parsers are not yet available. One plausible way forward is to limit the intensive work to a small-sized corpus. However, with little systematic enquiry about approaches to corpus construction, it is unclear how robust a small corpus can be made. The current study examines the potential of a non-sequential sampling protocol for small corpus development (NSP-SCD) through a cross-corpora and within-corpus analysis. A corpus comprising 17,584 words was developed by applying the protocol to a larger corpus of 150,595 words from children's books for 3-to-10-year-olds. While the larger corpus will by definition have more instances of unique words and unique orthographic units, still, the selectively sampled small corpus approximated the larger corpus for lexical and orthographic diversity and was equivalent for orthographic representation and word length. Psycholinguistic complexity increased by book level and varied by parts of speech. Finally, in a robustness check of lexical diversity, the non-sequentially sampled small corpus was more efficient compared to a same-sized corpus constructed by simply using all sentences from a few books (402 books vs. seven books). If a small corpus must be used then non-sequential sampling from books stratified by book level makes the corpus statistics better approximate what is found in larger corpora. Overall, the protocol shows promise as a tool to advance the science of child language acquisition in understudied languages.
Keyphrases
  • randomized controlled trial
  • mental health
  • public health
  • machine learning
  • risk assessment
  • deep learning
  • climate change
  • wastewater treatment