Paper: Memory-Based Learning: Using Similarity For Smoothing

ACL ID P97-1056
Title Memory-Based Learning: Using Similarity For Smoothing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1997

This paper analyses the relation between the use of similarity in Memory-Based Learning and the notion of backed-off smoothing in statistical language model- ing. 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 au- tomatically specifying a suitable domain- specific hierarchy between most specific and most general conditioning information without the need for a large number of pa- rameters. We report two applications of this approach: PP-attachment and POS- tagging. Our method achieves state-of-the- art performance in both domains, and al- lows the easy integration of diverse infor- mation sources, such as rich lexical repre- sentations.