Resolving PP attachment Ambiguities with Memory-Based Learning
Jakub Zavrel, Walter M. P. Daelemans, Jorn Veenstra · Research portal (Tilburg University) · 1997
In this paper we describe the application of Memory-Based Learning to the problem of Prepositional Phrase attachment disambiguation. We compare Memory-Based Learning, which stores examples in memory and generalizes by using intelligent similarity metrics, with a number of recently proposed statistical methods that are well suited to large numbers of features. We evaluate our methods on a common benchmark dataset and show that our method compares favorably to previous methods, and is well-suited to incorporating various unconventional representations of word patterns such as value difference metrics and Lexical Space. Introduction A central issue in natural language analysis is structural ambiguity resolution. A sentence is structurally ambiguous when it can be assigned more than one syntactic structure. The drosophila of structural ambiguity resolution is Prepositional Phrase (PP) attachment. Several sources of information can be used to resolve PP attachment ambiguity. Psycholinguist...