Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French

TL;DR

This paper introduces a decision list-based statistical method for resolving lexical ambiguities, specifically applied to restoring missing accents in Spanish and French text, combining local syntax and collocational evidence.

Contribution

It presents a novel, efficient decision procedure that uses only the most relevant evidence for disambiguation, avoiding complex dependency modeling.

Findings

Effective in accent restoration for Spanish and French

Outperforms previous ambiguity resolution methods

Simple yet accurate disambiguation approach

Abstract

This paper presents a statistical decision procedure for lexical ambiguity resolution. The algorithm exploits both local syntactic patterns and more distant collocational evidence, generating an efficient, effective, and highly perspicuous recipe for resolving a given ambiguity. By identifying and utilizing only the single best disambiguating evidence in a target context, the algorithm avoids the problematic complex modeling of statistical dependencies. Although directly applicable to a wide class of ambiguities, the algorithm is described and evaluated in a realistic case study, the problem of restoring missing accents in Spanish and French text.