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

Pius von Däniken, Mark Cieliebak

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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
Editors:
Leon Derczynski, Wei Xu, Alan Ritter, Tim Baldwin
Venue:
WNUT
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, WNUT 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W17-4422.pdf
Data
WNUT 2017