@inproceedings{xiong-etal-2019-deep,
title = "A Deep Learning-Based System for {P}harma{C}o{NER}",
author = "Xiong, Ying and
Shen, Yedan and
Huang, Yuanhang and
Chen, Shuai and
Tang, Buzhou and
Wang, Xiaolong and
Chen, Qingcai and
Yan, Jun and
Zhou, Yi",
editor = "Jin-Dong, Kim and
Claire, N{\'e}dellec and
Robert, Bossy and
Louise, Del{\'e}ger",
booktitle = "Proceedings of the 5th Workshop on BioNLP Open Shared Tasks",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5706",
doi = "10.18653/v1/D19-5706",
pages = "33--37",
abstract = "The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical {\&} drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2). We developed a pipeline system based on deep learning methods for this shared task, specifically, a subsystem based on BERT (Bidirectional Encoder Representations from Transformers) for NER offset and entity classification and a subsystem based on Bpool (Bi-LSTM with max/mean pooling) for concept indexing. Evaluation conducted on the shared task data showed that our system achieves a micro-average F1-score of 0.9105 on track 1 and a micro-average F1-score of 0.8391 on track 2.",
}
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<abstract>The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical & drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2). We developed a pipeline system based on deep learning methods for this shared task, specifically, a subsystem based on BERT (Bidirectional Encoder Representations from Transformers) for NER offset and entity classification and a subsystem based on Bpool (Bi-LSTM with max/mean pooling) for concept indexing. Evaluation conducted on the shared task data showed that our system achieves a micro-average F1-score of 0.9105 on track 1 and a micro-average F1-score of 0.8391 on track 2.</abstract>
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%0 Conference Proceedings
%T A Deep Learning-Based System for PharmaCoNER
%A Xiong, Ying
%A Shen, Yedan
%A Huang, Yuanhang
%A Chen, Shuai
%A Tang, Buzhou
%A Wang, Xiaolong
%A Chen, Qingcai
%A Yan, Jun
%A Zhou, Yi
%Y Jin-Dong, Kim
%Y Claire, Nédellec
%Y Robert, Bossy
%Y Louise, Deléger
%S Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F xiong-etal-2019-deep
%X The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical & drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2). We developed a pipeline system based on deep learning methods for this shared task, specifically, a subsystem based on BERT (Bidirectional Encoder Representations from Transformers) for NER offset and entity classification and a subsystem based on Bpool (Bi-LSTM with max/mean pooling) for concept indexing. Evaluation conducted on the shared task data showed that our system achieves a micro-average F1-score of 0.9105 on track 1 and a micro-average F1-score of 0.8391 on track 2.
%R 10.18653/v1/D19-5706
%U https://aclanthology.org/D19-5706
%U https://doi.org/10.18653/v1/D19-5706
%P 33-37
Markdown (Informal)
[A Deep Learning-Based System for PharmaCoNER](https://aclanthology.org/D19-5706) (Xiong et al., BioNLP 2019)
ACL
- Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, and Yi Zhou. 2019. A Deep Learning-Based System for PharmaCoNER. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 33–37, Hong Kong, China. Association for Computational Linguistics.