Choosing the Word Most Typical in Context Using a Lexical Co-occurrence Network

TL;DR

This paper introduces a statistical method using lexical co-occurrence networks to improve the selection of the most contextually typical synonym, demonstrating enhanced performance with second-order relations.

Contribution

It presents a novel approach applying lexical co-occurrence networks for lexical choice, emphasizing the role of second-order relations in context modeling.

Findings

Second-order co-occurrence relations improve lexical choice accuracy.

The approach outperforms baseline methods on a large corpus.

Inclusion of network-based context representation enhances synonym selection.

Abstract

This paper presents a partial solution to a component of the problem of lexical choice: choosing the synonym most typical, or expected, in context. We apply a new statistical approach to representing the context of a word through lexical co-occurrence networks. The implementation was trained and evaluated on a large corpus, and results show that the inclusion of second-order co-occurrence relations improves the performance of our implemented lexical choice program.