Towards a bilingual Alzheimer's disease terminology acquisition using a parallel corpus

We present in this paper a method for acquiring a bilingual terminology concerning the Alzheimer's disease using a parallel corpus. NLP techniques are used for parsing English and French texts in order to extract candidate terms. These terms are then matched automatically using an approach that combines two alignment techniques: one based on the calculation of an association score between two terms, and another technique based on the calculation of morphological similarity. This method provided good results on an Alzheimer's disease related corpus with a precision of 73%.