MEGAnno: Exploratory Labeling for NLP in Computational Notebooks
This addresses the need for more integrated labeling tools for NLP researchers and practitioners, though it appears incremental as it builds on existing labeling concepts.
The paper tackles the problem of limited labeling tools in NLP by introducing MEGAnno, a framework that supports an iterative ML workflow including data exploration and model development, demonstrated through a sentiment analysis use case.
We present MEGAnno, a novel exploratory annotation framework designed for NLP researchers and practitioners. Unlike existing labeling tools that focus on data labeling only, our framework aims to support a broader, iterative ML workflow including data exploration and model development. With MEGAnno's API, users can programmatically explore the data through sophisticated search and automated suggestion functions and incrementally update task schema as their project evolve. Combined with our widget, the users can interactively sort, filter, and assign labels to multiple items simultaneously in the same notebook where the rest of the NLP project resides. We demonstrate MEGAnno's flexible, exploratory, efficient, and seamless labeling experience through a sentiment analysis use case.