Attaching Multiple Prepositional Phrases: Generalized Backed-off Estimation

TL;DR

This paper extends backed-off estimation techniques to handle multiple prepositional phrase attachments, addressing challenges of data sparsity and syntactic ambiguity by reusing information from initial PP attachments.

Contribution

It introduces an algorithm that effectively manages multiple PP attachments by leveraging data from earlier attachments to improve accuracy.

Findings

Successfully extended backed-off estimation to multiple PP attachment

Reduced data sparsity issues in multiple PP attachment

Improved attachment accuracy through re-use of initial PP data

Abstract

There has recently been considerable interest in the use of lexically-based statistical techniques to resolve prepositional phrase attachments. To our knowledge, however, these investigations have only considered the problem of attaching the first PP, i.e., in a [V NP PP] configuration. In this paper, we consider one technique which has been successfully applied to this problem, backed-off estimation, and demonstrate how it can be extended to deal with the problem of multiple PP attachment. The multiple PP attachment introduces two related problems: sparser data (since multiple PPs are naturally rarer), and greater syntactic ambiguity (more attachment configurations which must be distinguished). We present and algorithm which solves this problem through re-use of the relatively rich data obtained from first PP training, in resolving subsequent PP attachments.