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        <title>Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text</title>
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        <namePart type="given">Renzo</namePart>
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            <namePart type="given">Kim</namePart>
            <namePart type="family">Jin-Dong</namePart>
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        <name type="personal">
            <namePart type="given">Nédellec</namePart>
            <namePart type="family">Claire</namePart>
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    <abstract>In this work, we introduce a Deep Learning architecture for pharmaceutical and chemical Named Entity Recognition in Spanish clinical cases texts. We propose a hybrid model approach based on two Bidirectional Long Short-Term Memory (Bi-LSTM) network and Conditional Random Field (CRF) network using character, word, concept and sense embeddings to deal with the extraction of semantic, syntactic and morphological features. The approach was evaluated on the PharmaCoNER Corpus obtaining an F-measure of 85.24% for subtask 1 and 49.36% for subtask2. These results prove that deep learning methods with specific domain embedding representations can outperform the state-of-the-art approaches.</abstract>
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%0 Conference Proceedings
%T Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text
%A Rivera, Renzo
%A Martínez, Paloma
%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 rivera-martinez-2019-deep
%X In this work, we introduce a Deep Learning architecture for pharmaceutical and chemical Named Entity Recognition in Spanish clinical cases texts. We propose a hybrid model approach based on two Bidirectional Long Short-Term Memory (Bi-LSTM) network and Conditional Random Field (CRF) network using character, word, concept and sense embeddings to deal with the extraction of semantic, syntactic and morphological features. The approach was evaluated on the PharmaCoNER Corpus obtaining an F-measure of 85.24% for subtask 1 and 49.36% for subtask2. These results prove that deep learning methods with specific domain embedding representations can outperform the state-of-the-art approaches.
%R 10.18653/v1/D19-5707
%U https://aclanthology.org/D19-5707
%U https://doi.org/10.18653/v1/D19-5707
%P 38-46

Markdown (Informal)

[Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text](https://aclanthology.org/D19-5707) (Rivera & Martínez, BioNLP 2019)

• Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text (Rivera & Martínez, BioNLP 2019)

ACL

• Renzo Rivera and Paloma Martínez. 2019. Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 38–46, Hong Kong, China. Association for Computational Linguistics.