@inproceedings{zhang-etal-2022-meganno,
title = "{MEGA}nno: Exploratory Labeling for {NLP} in Computational Notebooks",
author = "Zhang, Dan and
Kim, Hannah and
Li Chen, Rafael and
Kandogan, Eser and
Hruschka, Estevam",
editor = "Dragut, Eduard and
Li, Yunyao and
Popa, Lucian and
Vucetic, Slobodan and
Srivastava, Shashank",
booktitle = "Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dash-1.1",
pages = "1--7",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T MEGAnno: Exploratory Labeling for NLP in Computational Notebooks
%A Zhang, Dan
%A Kim, Hannah
%A Li Chen, Rafael
%A Kandogan, Eser
%A Hruschka, Estevam
%Y Dragut, Eduard
%Y Li, Yunyao
%Y Popa, Lucian
%Y Vucetic, Slobodan
%Y Srivastava, Shashank
%S Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F zhang-etal-2022-meganno
%X 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.
%U https://aclanthology.org/2022.dash-1.1
%P 1-7
Markdown (Informal)
[MEGAnno: Exploratory Labeling for NLP in Computational Notebooks](https://aclanthology.org/2022.dash-1.1) (Zhang et al., DaSH 2022)
ACL
- Dan Zhang, Hannah Kim, Rafael Li Chen, Eser Kandogan, and Estevam Hruschka. 2022. MEGAnno: Exploratory Labeling for NLP in Computational Notebooks. In Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances), pages 1–7, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.