@inproceedings{almgren-etal-2016-named,
title = "Named Entity Recognition in {S}wedish Health Records with Character-Based Deep Bidirectional {LSTM}s",
author = "Almgren, Simon and
Pavlov, Sean and
Mogren, Olof",
editor = "Ananiadou, Sophia and
Batista-Navarro, Riza and
Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Thompson, Paul",
booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-5104",
pages = "30--39",
abstract = "We propose an approach for named entity recognition in medical data, using a character-based deep bidirectional recurrent neural network. Such models can learn features and patterns based on the character sequence, and are not limited to a fixed vocabulary. This makes them very well suited for the NER task in the medical domain. Our experimental evaluation shows promising results, with a 60{\%} improvement in F 1 score over the baseline, and our system generalizes well between different datasets.",
}
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<abstract>We propose an approach for named entity recognition in medical data, using a character-based deep bidirectional recurrent neural network. Such models can learn features and patterns based on the character sequence, and are not limited to a fixed vocabulary. This makes them very well suited for the NER task in the medical domain. Our experimental evaluation shows promising results, with a 60% improvement in F 1 score over the baseline, and our system generalizes well between different datasets.</abstract>
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%0 Conference Proceedings
%T Named Entity Recognition in Swedish Health Records with Character-Based Deep Bidirectional LSTMs
%A Almgren, Simon
%A Pavlov, Sean
%A Mogren, Olof
%Y Ananiadou, Sophia
%Y Batista-Navarro, Riza
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Thompson, Paul
%S Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F almgren-etal-2016-named
%X We propose an approach for named entity recognition in medical data, using a character-based deep bidirectional recurrent neural network. Such models can learn features and patterns based on the character sequence, and are not limited to a fixed vocabulary. This makes them very well suited for the NER task in the medical domain. Our experimental evaluation shows promising results, with a 60% improvement in F 1 score over the baseline, and our system generalizes well between different datasets.
%U https://aclanthology.org/W16-5104
%P 30-39
Markdown (Informal)
[Named Entity Recognition in Swedish Health Records with Character-Based Deep Bidirectional LSTMs](https://aclanthology.org/W16-5104) (Almgren et al., 2016)
ACL