RDRCE: Combining machine learning and knowledge acquisition

Abstract

We present a new interactive workbench RDRCE (RDR Case Explorer) to facilitate the combination of Machine Learning and manual Knowledge Acquisition for Natural Language Processing problems. We show how to use Brill's well regarded transformational learning approach and convert its results into an RDR tree. RDRCE then strongly guides the systematic inspection of the generated RDR tree in order to further refine and improve it by manually adding more rules. Furthermore, RDRCE also helps in quickly recognising potential noise in the training data and allows to deal with noise effectively. Finally, we present a first study using RDRCE to build a high-quality Part-of-Speech tagger for English. After some 60 hours of manual knowledge acquisition, we already exceed slightly the state-of-the art performance on unseen benchmark test data and the fruits of some 15 years of further research in learning methods for Part-of-Speech taggers. © 2010 Springer-Verlag Berlin Heidelberg.