Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets

Pius von Däniken, Mark Cieliebak


Abstract
We present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data. We describe two modifications of a basic neural network architecture for sequence tagging. First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task. Then, we propose a way to incorporate sentence level features. Our system uses both methods and ranked second for entity level annotations, achieving an F1-score of 40.78, and second for surface form annotations, achieving an F1-score of 39.33.
Anthology ID:
W17-4422
Volume:
Proceedings of the 3rd Workshop on Noisy User-generated Text
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
166–171
Language:
URL:
https://aclanthology.org/W17-4422
DOI:
10.18653/v1/W17-4422
Bibkey:
Cite (ACL):
Pius von Däniken and Mark Cieliebak. 2017. Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 166–171, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets (von Däniken & Cieliebak, 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-4422.pdf
Data
WNUT 2017