A Hierarchical Approach to exploiting Multiple Datasets from TalkBank
Man Ho Wong
TalkBank is an online database that facilitates the sharing of linguistics
research data. However, the existing TalkBank's API has limited data filtering
and batch processing capabilities. To overcome these limitations, this paper
introduces a pipeline framework that employs a hierarchical search approach,
enabling efficient complex data selection. This approach involves a quick
preliminary screening of relevant corpora that a researcher may need, and then
perform an in-depth search for target data based on specific criteria. The
identified files are then indexed, providing easier access for future analysis.
Furthermore, the paper demonstrates how data from different studies curated
with the framework can be integrated by standardizing and cleaning metadata,
allowing researchers to extract insights from a large, integrated dataset.
While being designed for TalkBank, the framework can also be adapted to process
data from other open-science platforms.