UQAM-NTL: Named entity recognition in Twitter messages

Ngoc Tan Le, Fatma Mallek, Fatiha Sadat


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
Venues:
WNUT | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
197–202
Language:
URL:
https://www.aclweb.org/anthology/W16-3926
DOI:
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/W16-3926.pdf