A task-based comparison of information extraction pattern models
Mark Greenwood, Mark Stevenson · 2007
Several recent approaches to Information Extraction (IE) have used dependency trees as the basis for an extraction pattern representation. These approaches have used a variety of pattern models (schemes which define the parts of the dependency tree which can be used to form extraction patterns). Previous comparisons of these pattern models are limited by the fact that they have used indirect tasks to evaluate each model. This limitation is addressed here in an experiment which compares four pattern models using an unsupervised learning algorithm and a standard IE scenario. It is found that there is a wide variation between the models' performance and suggests that one model is the most useful for IE.