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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 RNN embeddings for identifying difficult to understand medical words Pylieva, Hanna; Chernodub, Artem; Grabar, Natalia... In: ACL Workshop on Biomedical Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-02371219 ; ACL Workshop on Biomedical Natural Language Processing, Aug 2019, Florence, Italy (2019) BASE Show details 2 Improving Automatic Categorization of Technical vs. Laymen Medical Words using FastText Word Embeddings Pylieva, Hanna; Chernodub, Artem; Grabar, Natalia; Hamon, Thierry In: 1st International Workshop on Informatics & Data-Driven Medicine (IDDM 2018) ; https://halshs.archives-ouvertes.fr/halshs-01968357 ; 1st International Workshop on Informatics & Data-Driven Medicine (IDDM 2018), Nov 2018, Lviv, Ukraine (2018) Abstract: International audience ; 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). Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [SHS.INFO]Humanities and Social Sciences/Library and information sciences; difficulty detection; text simplification; word embeddings URL: https://halshs.archives-ouvertes.fr/halshs-01968357/file/pylieva-IDDM2018.pdf https://halshs.archives-ouvertes.fr/halshs-01968357 https://halshs.archives-ouvertes.fr/halshs-01968357/document BASE Hide details

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