A semi-supervised Learning Approach to find equivalent long-string Organization Names - Bordignon, Frédérique; Turenne, Nicolas; Feugueur, Yann (HAL (Le Centre pour la Communication Scientifique Directe), 2016)
Background: A platform called Opalia has been built to propose free access to all publications about a laboratory for a given range of years. This platform makes indexing of a corpus of a scientific article of a given lab. But in the French research system, a lab includes researchers from different organizations in the same unit generally called. UMR. Authors can write their laboratory names differently. Aim: Sorting a set of labels that is noisy can be seen as a binary classification into positives and leave negatives strings. We propose to use a cascade processing with the help of tagging some positive strings to build a relevant space of features that helps classification into good labels.