Erase and Rewind: Manual Correction of NLP Output through a Web Interface

Valentino Frasnelli, Lorenzo Bocchi, Alessio Palmero Aprosio


Abstract
In this paper, we present Tintful, an NLP annotation software that can be used both to manually annotate texts and to fix mistakes in NLP pipelines, such as Stanford CoreNLP. Using a paradigm similar to wiki-like systems, a user who notices some wrong annotation can easily fix it and submit the resulting (and right) entry back to the tool developers. Moreover, Tintful can be used to easily annotate data from scratch. The input documents do not need to be in a particular format: starting from the plain text, the sentences are first annotated with CoreNLP, then the user can edit the annotations and submit everything back through a user-friendly interface.
Anthology ID:
2021.acl-demo.13
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
Month:
August
Year:
2021
Address:
Online
Editors:
Heng Ji, Jong C. Park, Rui Xia
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
107–113
Language:
URL:
https://aclanthology.org/2021.acl-demo.13
DOI:
10.18653/v1/2021.acl-demo.13
Bibkey:
Cite (ACL):
Valentino Frasnelli, Lorenzo Bocchi, and Alessio Palmero Aprosio. 2021. Erase and Rewind: Manual Correction of NLP Output through a Web Interface. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 107–113, Online. Association for Computational Linguistics.
Cite (Informal):
Erase and Rewind: Manual Correction of NLP Output through a Web Interface (Frasnelli et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-demo.13.pdf
Video:
 https://aclanthology.org/2021.acl-demo.13.mp4