Abstract
Correcting errors in a data set is a critical issue. This task can be either hand-made by experts, or by crowdsourcing methods, or automatically done using algorithms. Although the rate of errors present in the JeuxDeMots network is rather low, it is important to reduce it. We present here automatic methods for detecting potential secondary errors that would result from automatic inference mechanisms when they rely on an initial error manually detected. Encouraging results also invite us to consider strategies that would automatically detect “erroneous” initial relations, which could lead to the automatic detection of the majority of errors in the network.- Anthology ID:
- R17-1056
- Volume:
- Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 424–430
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-049-6_056
- DOI:
- 10.26615/978-954-452-049-6_056
- Cite (ACL):
- Mathieu Lafourcade, Alain Joubert, and Nathalie Le Brun. 2017. If mice were reptiles, then reptiles could be mammals or How to detect errors in the JeuxDeMots lexical network?. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 424–430, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- If mice were reptiles, then reptiles could be mammals or How to detect errors in the JeuxDeMots lexical network? (Lafourcade et al., RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_056