  Event classification at sentence level is an important Information Extraction
task with applications in several NLP, IR, and personalization systems.
Multi-label binary relevance (BR) are the state-of-art methods. In this work,
we explored new multi-label methods known for capturing relations between event
types. These new methods, such as the ensemble Chain of Classifiers, improve
the F1 on average across the 6 labels by 2.8% over the Binary Relevance. The
low occurrence of multi-label sentences motivated the reduction of the hard
imbalanced multi-label classification problem with low number of occurrences of
multiple labels per instance to an more tractable imbalanced multiclass problem
with better results (+ 4.6%). We report the results of adding new features,
such as sentiment strength, rhetorical signals, domain-id (source-id and date),
and key-phrases in both single-label and multi-label event classification
scenarios.
