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
3 UP13 - Université Paris 13

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).

Mots clés: text simplification; difficulty detection; word embeddings

Domaines: Sciences de l'information et de la communication; Intelligence artificielle [cs.AI]

Hanna Pylieva, Artem Chernodub, Natalia Grabar, Thierry Hamon. Improving Automatic Categorization of Technical vs. Laymen Medical Words using FastText Word Embeddings. 1st International Workshop on Informatics & Data-Driven Medicine (IDDM 2018), Nov 2018, Lviv, Ukraine. ⟨halshs-01968357⟩