Improving Automatic Categorization of Technical vs. Laymen Medical Words using FastText Word Embeddings

Hanna Pylieva; Artem Chernodub; Natalia Grabar; Thierry Hamon

Improving Automatic Categorization of Technical vs. Laymen Medical Words using FastText Word Embeddings

Hanna Pylieva Artem Chernodub Natalia Grabar ¹ Thierry Hamon ^{2, 3}

1 STL - Savoirs, Textes, Langage (STL) - UMR 8163

2 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

Abstract : Detection of difficult for understanding words is a crucial task for ensuring the proper understanding of medical texts such as diagnoses and drug instructions. In this paper, we study usage of recently developed word embeddings, which contain context information for words together with other linguistic and non-linguistic features, for improving the detection of difficult medical words. We propose new cross-validation scenarios in order to test the generalization ability of the medical words difficulty detection from different perspectives and provide the experimental study of previously used methods for feature extraction together with recently proposed FastText embeddings. We found that for known words and unknown users FastText embeddings surely improves the detection of word understandability reaching 85.9 F-score (up to 2.9 F-score improvement).

Keywords : text simplification difficulty detection word embeddings

Document type :

Conference papers

Domain :

Humanities and Social Sciences / Library and information sciences

Computer Science [cs] / Artificial Intelligence [cs.AI]

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https://halshs.archives-ouvertes.fr/halshs-01968357
Contributor : Natalia Grabar <>
Submitted on : Wednesday, January 2, 2019 - 3:47:38 PM
Last modification on : Friday, May 3, 2019 - 4:56:48 PM
Long-term archiving on : Wednesday, April 3, 2019 - 4:09:31 PM

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Improving Automatic Categorization of Technical vs. Laymen Medical Words using FastText Word Embeddings

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