Identifying and Ordering Scalar Adjectives using Lexical Substitution

Abstract

Lexical semantics provides many important resources in natural language processing, despite the recent preferences for distributional methods. In this dissertations we investigate an under-represented lexical relationship, that of scalarity. We define scalarity as it relates to adjectives and introduce novel methods to identify words belonging to a particular scale and to order those words once they are found. This information has important uses in both traditional linguistics as well as natural language processing. We focus on solving both these problems using lexical substitution, a technique that allows us to determine the best substitute word for a given word in a sentence. We also produce two new datasets: a gold standard of scalar adjectives for use in the development and evaluation of methods like the ones introduces here, and a test set of indirect question-answer pairs, one possible application of scalar adjectives.