Identification of Mentions and Relations between Bacteria and Biotope from PubMed Abstracts

This paper presents our participation in the Bacteria/Biotope track from the 2016 BioNLP Shared-Task. Our methods rely on a combination of distinct machinelearning and rule-based systems. We used CRF and post-processing rules to identify mentions of bacteria and biotopes, a rulebased approach to normalize the concepts in the ontology and the taxonomy, and SVM to identify relations between bacteria and biotopes. On the test datasets, we achieved similar results to those obtained on the development datasets: on the categorization task, precision of 0.503 (gold standard entities) and SER of 0.827 (both NER and categorization); on the event relation task, F-measure of 0.485 (gold standard entities, ranking third out of 11) and of 0.192 (both NER and event relation, ranking first); on the knowledgebased task, mean references of 0.771 (gold standard entities) and of 0.202 (both NER, categorization and event relation).