Complex tasks like question answering need to be able to identify events in text and the relations among those events. We show that this event identification task and a related task, identifying the semantic class of these events, can both be formulated as classification problems in a word-chunking paradigm. We introduce a variety of linguistically motivated features for this task and then train a system that is able to identify events with a precision of 82 % and a recall of 71%. We then show a variety of analyses of this model, and their implications for the event identification task.
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.