This paper presents TIPSem, a system to extract temporal information from natural language texts for English and Spanish. TIPSem, learns CRF models from training data. Although the used features include different language analysis levels, the approach is focused on semantic information. For Spanish, TIPSem achieved the best F1 score in all the tasks. For English, it obtained the best F1 in tasks B (events) and D (event-dct links); and was among the best systems in the rest.
['Hector Llorens', 'Estela Saquete', 'Borja Navarro']
TIPSem (English and Spanish): Evaluating CRFs and Semantic Roles in TempEval-2