  Prior work has proposed effective methods to learn event representations that
can capture syntactic and semantic information over text corpus, demonstrating
their effectiveness for downstream tasks such as script event prediction. On
the other hand, events extracted from raw texts lacks of commonsense knowledge,
such as the intents and emotions of the event participants, which are useful
for distinguishing event pairs when there are only subtle differences in their
surface realizations. To address this issue, this paper proposes to leverage
external commonsense knowledge about the intent and sentiment of the event.
Experiments on three event-related tasks, i.e., event similarity, script event
prediction and stock market prediction, show that our model obtains much better
event embeddings for the tasks, achieving 78% improvements on hard similarity
task, yielding more precise inferences on subsequent events under given
contexts, and better accuracies in predicting the volatilities of the stock
market.
