Fig. 4
From: Learning adaptive representations for entity recognition in the biomedical domain
Depiction of the proposed system and other features aggregation schemes. The OGER annotator retrieves candidate entities from input texts (Deoxyribonucleic acid in the example). Then, different set of features associate to the candidate entity are computed (e.g. affixes and spectrum) or extracted (word2vec), producing multiple feature vectors. Consequently, the features aggregation schema defines the final representation as (i) a single base representation, (ii) the concatenation of base feature vectors, and (iii) the principled combination obtained through a MKL algorithm or a NN (shown in Fig. 2). The resulting representation is used with a classifier to select the final class (entity or not)