<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pylieva-etal-2019-rnn">
    <titleInfo>
        <title>RNN Embeddings for Identifying Difficult to Understand Medical Words</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hanna</namePart>
        <namePart type="family">Pylieva</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Artem</namePart>
        <namePart type="family">Chernodub</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Natalia</namePart>
        <namePart type="family">Grabar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Thierry</namePart>
        <namePart type="family">Hamon</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 18th BioNLP Workshop and Shared Task</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="given">Bretonnel</namePart>
            <namePart type="family">Cohen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Patients and their families often require a better understanding of medical information provided by doctors. We currently address this issue by improving the identification of difficult to understand medical words. We introduce novel embeddings received from RNN - FrnnMUTE (French RNN Medical Understandability Text Embeddings) which allow to reach up to 87.0 F1 score in identification of difficult words. We also note that adding pre-trained FastText word embeddings to the feature set substantially improves the performance of the model which classifies words according to their difficulty. We study the generalizability of different models through three cross-validation scenarios which allow testing classifiers in real-world conditions: understanding of medical words by new users, and classification of new unseen words by the automatic models. The RNN - FrnnMUTE embeddings and the categorization code are being made available for the research.</abstract>
    <identifier type="citekey">pylieva-etal-2019-rnn</identifier>
    <identifier type="doi">10.18653/v1/W19-5011</identifier>
    <location>
        <url>https://aclanthology.org/W19-5011</url>
    </location>
    <part>
        <date>2019-08</date>
        <extent unit="page">
            <start>97</start>
            <end>104</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T RNN Embeddings for Identifying Difficult to Understand Medical Words
%A Pylieva, Hanna
%A Chernodub, Artem
%A Grabar, Natalia
%A Hamon, Thierry
%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 pylieva-etal-2019-rnn
%X Patients and their families often require a better understanding of medical information provided by doctors. We currently address this issue by improving the identification of difficult to understand medical words. We introduce novel embeddings received from RNN - FrnnMUTE (French RNN Medical Understandability Text Embeddings) which allow to reach up to 87.0 F1 score in identification of difficult words. We also note that adding pre-trained FastText word embeddings to the feature set substantially improves the performance of the model which classifies words according to their difficulty. We study the generalizability of different models through three cross-validation scenarios which allow testing classifiers in real-world conditions: understanding of medical words by new users, and classification of new unseen words by the automatic models. The RNN - FrnnMUTE embeddings and the categorization code are being made available for the research.
%R 10.18653/v1/W19-5011
%U https://aclanthology.org/W19-5011
%U https://doi.org/10.18653/v1/W19-5011
%P 97-104

Markdown (Informal)

[RNN Embeddings for Identifying Difficult to Understand Medical Words](https://aclanthology.org/W19-5011) (Pylieva et al., BioNLP 2019)

• RNN Embeddings for Identifying Difficult to Understand Medical Words (Pylieva et al., BioNLP 2019)

ACL

• Hanna Pylieva, Artem Chernodub, Natalia Grabar, and Thierry Hamon. 2019. RNN Embeddings for Identifying Difficult to Understand Medical Words. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 97–104, Florence, Italy. Association for Computational Linguistics.