  A lot of prior work on event extraction has exploited a variety of features
to represent events. Such methods have several drawbacks: 1) the features are
often specific for a particular domain and do not generalize well; 2) the
features are derived from various linguistic analyses and are error-prone; and
3) some features may be expensive and require domain expert. In this paper, we
develop a Chinese event extraction system that uses word embedding vectors to
represent language, and deep neural networks to learn the abstract feature
representation in order to greatly reduce the effort of feature engineering. In
addition, in this framework, we leverage large amount of unlabeled data, which
can address the problem of limited labeled corpus for this task. Our
experiments show that our proposed method performs better compared to the
system using rich language features, and using unlabeled data benefits the word
embeddings. This study suggests the potential of DNN and word embedding for the
event extraction task.
