Fine-Grained Simplification of Medical Documents.

Easy access to medical and health information for children, foreigners and patients is an important issue for the modern society and research. Indeed, misunderstanding of medical and health information by patients may have a negative impact on their healthcare process and health. Even if several simplification guidelines exist, they are difficult to use by medical experts (i.e. lack of time, difficulty to respect the criteria). Existing simplification systems mainly address some lexical or syntactic transformations. We propose to combine lexical and syntactic simplifications within one rule-based system and to make the process fine-grained thanks to a better control of the grammaticality of sentences.

Disambiguation of Medical Abbreviations in French with Supervised Methods.

Abbreviations are very frequent in medical and health documents but they convey opaque semantics. The association with their expanded forms, like Chronic obstructive pulmonary disease for COPD, may help their understanding. Yet, several abbreviations are ambiguous and have expanded forms possible. We propose to disambiguate the abbreviations in order to associate them with the proper expansion for a given context. We treat the problem through supervised categorization. We create reference data and test several algorithms. The descriptors are collected from lexical and syntactic contexts of abbreviations. The training is done on sentences containing expanded forms of abbreviations. The test is done on corpus built manually, in which the meaning of abbreviations is defined according to their contexts. Our approach shows up to 0.895 F-measure on training data and 0.773 on test data.