Memory-Based Learning: Using Similarity for Smoothing

This paper analyses the relation between the use of similarity in MemoryBased Learning and the notion of backed-off smoothing in statistical language modeling. We show that the two approaches are closely related, and we argue that feature weighting methods in the Memory-Based paradigm can offer the advantage of automatically specifying a suitable domain-specific hierarchy between most specific and most general conditioning information without the need for a large number of parameters. We report two applications of this approach: PP-attachment and POS-tagging. Our method achieves stateof -the-art performance in both domains, and allows the easy integration of diverse information sources, such as rich lexical representations. 1 Introduction Statistical approaches to disambiguation offer the advantage of making the most likely decision on the basis of available evidence. For this purpose a large number of probabilities has to be estimated from a training corpus. However, many possible co..