LexiClean: An annotation tool for rapid multi-task lexical normalisation

Tyler Bikaun, Tim French, Melinda Hodkiewicz, Michael Stewart, Wei Liu


Abstract
NLP systems are often challenged by difficulties arising from noisy, non-standard, and domain specific corpora. The task of lexical normalisation aims to standardise such corpora, but currently lacks suitable tools to acquire high-quality annotated data to support deep learning based approaches. In this paper, we present LexiClean, the first open-source web-based annotation tool for multi-task lexical normalisation. LexiClean’s main contribution is support for simultaneous in situ token-level modification and annotation that can be rapidly applied corpus wide. We demonstrate the usefulness of our tool through a case study on two sets of noisy corpora derived from the specialised-domain of industrial mining. We show that LexiClean allows for the rapid and efficient development of high-quality parallel corpora. A demo of our system is available at: https://youtu.be/P7_ooKrQPDU.
Anthology ID:
2021.emnlp-demo.25
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:
212–219
Language:
URL:
https://aclanthology.org/2021.emnlp-demo.25
DOI:
10.18653/v1/2021.emnlp-demo.25
Bibkey:
Cite (ACL):
Tyler Bikaun, Tim French, Melinda Hodkiewicz, Michael Stewart, and Wei Liu. 2021. LexiClean: An annotation tool for rapid multi-task lexical normalisation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 212–219, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
LexiClean: An annotation tool for rapid multi-task lexical normalisation (Bikaun et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-demo.25.pdf
Video:
 https://aclanthology.org/2021.emnlp-demo.25.mp4
Code
 nlp-tlp/lexiclean