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Conventional event extraction using machine learning usually employed pipeline approach to extract event and arguments independently. This approach needs training data which is sometimes difficult to obtain and is domain specific. Nested event structure is common in both open domain and domain specific texts. The underlying structure needs to be taken into consideration in extracting nested events which has been neglected by the pipeline approach. This paper intends to fill these gaps by proposing an event model that is data economical at the same time can extract nested event.
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