Advancing Lexical Semantics Analysis Using Deep Learning Models for Contemporary English Language Evolution

Abstract

Lexical semantics plays a vital role in the representation of meaning and contextual interpretation in natural language processing. However, with the rapid growth in digital communication media, language usage in modern English has evolved at a rapid pace, posing a challenge for conventional lexical semantic analysis techniques. Linguistic corpora suggest that in recent times, more than 70% of lexical variation in online communication media can be attributed to contextual variation, neologisms, or polysemous words, which cannot be effectively handled by conventional lexical semantic analysis techniques. In this regard, recent advances in deep learning frameworks for lexical semantic analysis can be seen as a solution for addressing this challenge in lexical semantics research. In this study, recent advances in lexical semantic analysis using transformer architectures, contextual embedding models, and neural language models for lexical semantic analysis will be discussed in detail. Empirical studies suggest that contextual deep learning frameworks for lexical semantic analysis can enhance semantic similarity and word sense disambiguation accuracy by 30%. Hence, it can be seen that recent advances in lexical semantic analysis using deep learning frameworks can be a potential solution for enhancing lexical semantics research..