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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W17-4422.pdf
- Data
- WNUT 2017