Word Sense Disambiguation Based on Mutual Information and Syntactic Patterns

TL;DR

This paper presents a hybrid unsupervised system for Word Sense Disambiguation that combines mutual information proximity and syntactic pattern matching, showing promising precision results.

Contribution

It introduces a novel combination of mutual information and syntactic pattern heuristics for WSD, demonstrating their effectiveness in unsupervised settings.

Findings

Mutual information heuristic achieved 0.58 precision and 0.35 recall.

Syntactic pattern heuristic achieved 0.80 precision and 0.25 recall.

Results suggest potential for further research into these techniques.

Abstract

This paper describes a hybrid system for WSD, presented to the English all-words and lexical-sample tasks, that relies on two different unsupervised approaches. The first one selects the senses according to mutual information proximity between a context word a variant of the sense. The second heuristic analyzes the examples of use in the glosses of the senses so that simple syntactic patterns are inferred. This patterns are matched against the disambiguation contexts. We show that the first heuristic obtains a precision and recall of .58 and .35 respectively in the all words task while the second obtains .80 and .25. The high precision obtained recommends deeper research of the techniques. Results for the lexical sample task are also provided.