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
Authors

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.