Abstract
In this paper, we introduce and demonstrate the online demo as well as the command line interface of a lexical normalization system (MoNoise) for a variety of languages. We further improve this model by using features from the original word for every normalization candidate. For comparison with future work, we propose the bundling of seven datasets in six languages to form a new benchmark, together with a novel evaluation metric which is particularly suitable for cross-dataset comparisons. MoNoise reaches a new state-of-art performance for six out of seven of these datasets. Furthermore, we allow the user to tune the ‘aggressiveness’ of the normalization, and show how the model can be made more efficient with only a small loss in performance. The online demo can be found on: http://www.robvandergoot.com/monoise and the corresponding code on: https://bitbucket.org/robvanderg/monoise/- Anthology ID:
- P19-3032
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Marta R. Costa-jussà, Enrique Alfonseca
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 201–206
- Language:
- URL:
- https://aclanthology.org/P19-3032
- DOI:
- 10.18653/v1/P19-3032
- Cite (ACL):
- Rob van der Goot. 2019. MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 201–206, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool (van der Goot, ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P19-3032.pdf
- Code
- robvanderg/cacheembeds