Nahyun Kwon, Hannah Kim, Sajjadur Rahman, Dan Zhang, Estevam Hruschka
Data-centric NLP is a highly iterative process requiring careful exploration of text data throughout entire model development lifecycle. Unfortunately, existing data exploration tools are not suitable to support data-centric NLP because of workflow discontinuity and lack of support for unstructured text. In response, we propose Weedle, a seamless and customizable exploratory text analysis system for data-centric NLP. Weedle is equipped with built-in text transformation operations and a suite of visual analysis features. With its widget, users can compose customizable dashboards interactively and programmatically in computational notebooks.