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        <title>Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?</title>
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    <abstract>We present two models for combining word and character embeddings for cause-of-death classification of verbal autopsy reports using the text of the narratives. We find that for smaller datasets (500 to 1000 records), adding character information to the model improves classification, making character-based CNNs a promising method for automated verbal autopsy coding.</abstract>
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%0 Conference Proceedings
%T Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?
%A Yan, Zhaodong
%A Jeblee, Serena
%A Hirst, Graeme
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F yan-etal-2019-character
%X We present two models for combining word and character embeddings for cause-of-death classification of verbal autopsy reports using the text of the narratives. We find that for smaller datasets (500 to 1000 records), adding character information to the model improves classification, making character-based CNNs a promising method for automated verbal autopsy coding.
%R 10.18653/v1/W19-5025
%U https://aclanthology.org/W19-5025
%U https://doi.org/10.18653/v1/W19-5025
%P 234-239

Markdown (Informal)

[Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?](https://aclanthology.org/W19-5025) (Yan et al., BioNLP 2019)

• Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives? (Yan et al., BioNLP 2019)

ACL

• Zhaodong Yan, Serena Jeblee, and Graeme Hirst. 2019. Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 234–239, Florence, Italy. Association for Computational Linguistics.