Fast NP Chunking Using Memory-Based Learning Techniques

In this paper we discuss the application of Memory-Based Learning (MBL) to fast NP chunking. We first discuss the application of a fast decision tree variant of MBL (IGTree) on the dataset described in (Ramshaw and Marcus, 1995), which consists of roughly 50,000 test and 200,000 train items. In a second series of experiments we used an architecture of two cascaded IGTrees. In the second level of this cascaded classifier we added context predictions as extra features so that incorrect predictions from the first level can be corrected, yielding a 97.2% generalisation accuracy with training and testing times in the order of seconds to minutes. The recall and precision for predicting NP chunks is respectively 94.3% and 89.0% 1 Introduction Language technology applications often require fast and accurate modules that can be developed and adapted rapidly (Daelemans et al., 1998a). In this paper we concentrate on a fast implementation of an NP chunking module. In chunking, a sentence is divi..