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A lot of prior work on event extraction has exploited a variety of featuresto represent events. Such methods have several drawbacks: 1) the features areoften specific for a particular domain and do not generalize well; 2) thefeatures are derived from various linguistic analyses and are error-prone; and3) some features may be expensive and require domain expert. In this paper, wedevelop a Chinese event extraction system that uses word embedding vectors torepresent language, and deep neural networks to learn the abstract featurerepresentation in order to greatly reduce the effort of feature engineering. Inaddition, in this framework, we leverage large amount of unlabeled data, whichcan address the problem of limited labeled corpus for this task. Ourexperiments show that our proposed method performs better compared to thesystem using rich language features, and using unlabeled data benefits the wordembeddings. This study suggests the potential of DNN and word embedding for theevent extraction task.

Chinese Event Extraction Using DeepNeural Network with Word Embedding