A Quadratic 0-1 Programming Approach for Word Sense Disambiguation

TL;DR

This paper introduces a quadratic 0-1 programming model to improve Word Sense Disambiguation by capturing inter-sense interactions and optimizing sense selection based on similarity and relatedness.

Contribution

It presents a novel quadratic 0-1 integer programming approach that models sense interactions for more accurate WSD, addressing limitations of previous methods.

Findings

The model effectively captures sense interactions in WSD.

It improves disambiguation accuracy over traditional methods.

The approach demonstrates the importance of sense-sense relatedness.

Abstract

Word Sense Disambiguation (WSD) is the task to determine the sense of an ambiguous word in a given context. Previous approaches for WSD have focused on supervised and knowledge-based methods, but inter-sense interactions patterns or regularities for disambiguation remain to be found. We argue the following cause as one of the major difficulties behind finding the right patterns: for a particular context, the intended senses of a sequence of ambiguous words are dependent on each other, i.e. the choice of one word's sense is associated with the choice of another word's sense, making WSD a combinatorial optimization problem.In this work, we approach the interactions between senses of different target words by a Quadratic 0-1 Integer Programming model (QIP) that maximizes the objective function consisting of (1) the similarity between candidate senses of a target word and the word in a context (the sense-word similarity), and (2) the semantic interactions (relatedness) between senses of all words in the context (the sense-sense relatedness).