Memory-Based Learning: Using Similarity for Smoothing

This paper analyses the relation between the use of similarity in Memory-Based 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...

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Published in:arXiv.org 1997-05
Main Authors: Zavrel, Jakub, Daelemans, Walter
Format: Article
Language:English
Subjects:
Information sources
Similarity
Smoothing
Weighting methods
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Daelemans, Walter
description This paper analyses the relation between the use of similarity in Memory-Based 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 state-of-the-art performance in both domains, and allows the easy integration of diverse information sources, such as rich lexical representations.
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subjects Information sources
Similarity
Smoothing
Weighting methods
title Memory-Based Learning: Using Similarity for Smoothing
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