RNN embeddings for identifying difficult to understand medical words

Pylieva, Hanna; Chernodub, Artem; Grabar, Natalia; Hamon, Thierry

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

Title :

RNN embeddings for identifying difficult to understand medical words

Author(s) :

Pylieva, Hanna [Auteur]
Chernodub, Artem [Auteur]
Grabar, Natalia [Auteur]

Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Hamon, Thierry [Auteur]
Université Paris 13 [UP13]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]

Conference title :

ACL Workshop on Biomedical Natural Language Processing

City :

Florence

Country :

Italie

Start date of the conference :

2019-08-01

English keyword(s) :

Medical word understanding
RNN
Medical domain
Embeddings

HAL domain(s) :

Informatique [cs]
Informatique [cs]/Informatique et langage [cs.CL]

English abstract : [en]

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 ...
Show more >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 ac- cording 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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Communication, Literacy, Education, Accessibility, Readability

Collections :

Savoirs, Textes, Langage (STL) - UMR 8163

Source :

Harvested from HAL

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