Abstract
This paper describes our system used in the 2nd Workshop on Noisy User-generated Text (WNUT) shared task for Named Entity Recognition (NER) in Twitter, in conjunction with Coling 2016. Our system is based on supervised machine learning by applying Conditional Random Fields (CRF) to train two classifiers for two evaluations. The first evaluation aims at predicting the 10 fine-grained types of named entities; while the second evaluation aims at predicting no type of named entities. The experimental results show that our method has significantly improved Twitter NER performance.- Anthology ID:
- W16-3926
- Volume:
- Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Bo Han, Alan Ritter, Leon Derczynski, Wei Xu, Tim Baldwin
- Venue:
- WNUT
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 197–202
- Language:
- URL:
- https://aclanthology.org/W16-3926
- DOI:
- Cite (ACL):
- Ngoc Tan Le, Fatma Mallek, and Fatiha Sadat. 2016. UQAM-NTL: Named entity recognition in Twitter messages. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 197–202, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- UQAM-NTL: Named entity recognition in Twitter messages (Le et al., WNUT 2016)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/W16-3926.pdf