Yedan Shen
2019
A Deep Learning-Based System for PharmaCoNER
Ying Xiong
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Yedan Shen
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Yuanhang Huang
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Shuai Chen
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Buzhou Tang
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Xiaolong Wang
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Qingcai Chen
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Jun Yan
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Yi Zhou
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
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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Co-authors
- Ying Xiong 1
- Yuanhang Huang 1
- Shuai Chen 1
- Buzhou Tang 1
- Xiaolong Wang 1
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