A Deep Learning-Based System for PharmaCoNER

Ying Xiong; Yedan Shen; Yuanhang Huang; Shuai Chen; Buzhou Tang; Xiaolong Wang; Qingcai Chen; Jun Yan; Yi Zhou

doi:10.18653/v1/D19-5706

A Deep Learning-Based System for PharmaCoNER

Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou

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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.

Anthology ID:: D19-5706
Volume:: Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
Month:: November
Year:: 2019
Address:: Hong Kong, China
Editors:: Kim Jin-Dong, Nédellec Claire, Bossy Robert, Deléger Louise
Venue:: BioNLP
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 33–37
Language:
URL:: https://aclanthology.org/D19-5706/
DOI:: 10.18653/v1/D19-5706
Bibkey:
Cite (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.
Cite (Informal):: A Deep Learning-Based System for PharmaCoNER (Xiong et al., BioNLP 2019)
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PDF:: https://aclanthology.org/D19-5706.pdf

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@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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%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

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Markdown (Informal)

[A Deep Learning-Based System for PharmaCoNER](https://aclanthology.org/D19-5706/) (Xiong et al., BioNLP 2019)

A Deep Learning-Based System for PharmaCoNER (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.