Disambiguation of Super Parts of Speech (or Supertags): Almost Parsing

TL;DR

This paper introduces techniques for disambiguating supertags in lexicalized grammar formalism, improving parsing efficiency by leveraging local lexical information and dependency models.

Contribution

It presents novel disambiguation methods for supertags using local lexical preferences and dependency-based models, enhancing parsing accuracy in LTAG.

Findings

Dependency-based models outperform unigram and trigram models

Supertag disambiguation improves parsing efficiency

Local lexical dependencies are effective for disambiguation

Abstract

In a lexicalized grammar formalism such as Lexicalized Tree-Adjoining Grammar (LTAG), each lexical item is associated with at least one elementary structure (supertag) that localizes syntactic and semantic dependencies. Thus a parser for a lexicalized grammar must search a large set of supertags to choose the right ones to combine for the parse of the sentence. We present techniques for disambiguating supertags using local information such as lexical preference and local lexical dependencies. The similarity between LTAG and Dependency grammars is exploited in the dependency model of supertag disambiguation. The performance results for various models of supertag disambiguation such as unigram, trigram and dependency-based models are presented.