  This paper reports on a set of experiments with different word embeddings to
initialize a state-of-the-art Bi-LSTM-CRF network for event detection and
classification in Italian, following the EVENTI evaluation exercise. The net-
work obtains a new state-of-the-art result by improving the F1 score for
detection of 1.3 points, and of 6.5 points for classification, by using a
single step approach. The results also provide further evidence that embeddings
have a major impact on the performance of such architectures.
