Automatic Event Classification Using Surface Text Features

Contents

Extracting events from documents quickly and accurately is an important goal for many tasks that require language understanding, such as question answering. We present a data-driven method for discovering events and their attributes in a corpus. We further demonstrate that a carefully chosen set of textual features, when used to train some well-known learning algorithms, can approach or exceed the accuracy of hand-crafted patterns for event classification, requiring far less time and expertise. The features can be gathered using lightweight text processing tools. Overall classification accuracy reaches 59.76% for a set of 11 event types.