@inproceedings{von-daniken-cieliebak-2017-transfer,
title = "Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets",
author = {von D{\"a}niken, Pius and
Cieliebak, Mark},
editor = "Derczynski, Leon and
Xu, Wei and
Ritter, Alan and
Baldwin, Tim",
booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4422",
doi = "10.18653/v1/W17-4422",
pages = "166--171",
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.",
}
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%0 Conference Proceedings
%T Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets
%A von Däniken, Pius
%A Cieliebak, Mark
%Y Derczynski, Leon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%S Proceedings of the 3rd Workshop on Noisy User-generated Text
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F von-daniken-cieliebak-2017-transfer
%X 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.
%R 10.18653/v1/W17-4422
%U https://aclanthology.org/W17-4422
%U https://doi.org/10.18653/v1/W17-4422
%P 166-171
Markdown (Informal)
[Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets](https://aclanthology.org/W17-4422) (von Däniken & Cieliebak, WNUT 2017)
ACL