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.
@article{arxiv.2301.03095,
title = {MEGAnno: Exploratory Labeling for NLP in Computational Notebooks},
author = {Dan Zhang and Hannah Kim and Rafael Li Chen and Eser Kandogan and Estevam Hruschka},
journal= {arXiv preprint arXiv:2301.03095},
year = {2023}
}
Comments
Data Science with Human-in-the-loop (DaSH) @ EMNLP 2022. Demo: https://meganno.github.io