This paper analyzes the contribution of semantic roles to TimeML event recognition and classification. For that purpose, an approach using conditional random fields with a variety of morphosyntactic features plus semantic roles features is developed and evaluated. Our system achieves an F1 of 81.4% in recognition and a 64.2% in classification. We demonstrate that the application of semantic roles improves the performance of the presented system, especially for nominal events.
REF presented an evaluation on event recognition and type classification.
TimeML Events Recognition and Classification: Learning CRF Models with Semantic Roles
COLING
Computer Science