ULISBOA at SemEval-2016 task 12: Extraction of temporal expressions, clinical events and relations using IBEnt

Abstract

This paper describes our approach to participate on SemEval2016 Task12: Clinical TempEval. Our system was based on IBEnt, a framework to identify chemical entities and their relations in text using machine learning techniques. This system has two modules, one to identify chemical entities, and other to identify the pairs of entities that represent a chemical interaction in the same text. In this work we adapted both IBEnt modules to extract temporal expressions, event expressions and relations, by creating new CRF classifiers, lists and rules. The top result of our system was in phase2 for the identification of narrative container relations where it obtained the maximum score of precision (0.823) from all participants.