Memory-Based Learning: Using Similarity for Smoothing

Details Memory-Based Learning: Using Similarity for Smoothing Memory-Based Learning: Using Similarity for Smoothing

1997-05-12

arxiv.org/abs/cmp-lg/9705010v1

Computer Science

Computation and Language

8 pages, uses aclap.sty, To appear in Proc. ACL/EACL 97

Scientific paper

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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