LightTag: Text Annotation Platform

Tal Perry


Abstract
Text annotation tools assume that their user’s goal is to create a labeled corpus. However,users view annotation as a necessary evil on the way to deliver business value through NLP.Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators. LightTag is a text annotation tool designed and built on that principle. This paper shares our design rationale, data modeling choices, and user interface decisions then illustrates how those choices serve the full NLP lifecycle.
Anthology ID:
2021.emnlp-demo.3
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Heike Adel, Shuming Shi
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
20–27
Language:
URL:
https://aclanthology.org/2021.emnlp-demo.3
DOI:
10.18653/v1/2021.emnlp-demo.3
Bibkey:
Cite (ACL):
Tal Perry. 2021. LightTag: Text Annotation Platform. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 20–27, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
LightTag: Text Annotation Platform (Perry, EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-demo.3.pdf
Video:
 https://aclanthology.org/2021.emnlp-demo.3.mp4