Analyzing Semantic Change through Lexical Replacements

TL;DR

This paper introduces a novel approach to modeling semantic change through lexical replacements, proposing an interpretable framework and evaluating LLaMa for semantic change detection.

Contribution

It presents a replacement schema to simulate semantic change and develops an interpretable model, also pioneering the use of LLaMa for this task.

Findings

Effective simulation of semantic change via lexical replacements

First application of LLaMa in semantic change detection

Development of an interpretable semantic change model

Abstract

Modern language models are capable of contextualizing words based on their surrounding context. However, this capability is often compromised due to semantic change that leads to words being used in new, unexpected contexts not encountered during pre-training. In this paper, we model \textit{semantic change} by studying the effect of unexpected contexts introduced by \textit{lexical replacements}. We propose a \textit{replacement schema} where a target word is substituted with lexical replacements of varying relatedness, thus simulating different kinds of semantic change. Furthermore, we leverage the replacement schema as a basis for a novel \textit{interpretable} model for semantic change. We are also the first to evaluate the use of LLaMa for semantic change detection.