A comparative analysis of Syntax-Driven and Semantics-Driven Approaches in Computational Linguistics

Abstract

This study examines the effectiveness of Syntax-Driven and Semantics-Driven methodologies within the field of Computational Linguistics. Despite previous research indicating the utility of both approaches, a significant gap exists in empirical evaluations juxtaposing their efficiency and applicability to real-world language processing tasks. Utilizing a mixed-methods framework, we conducted comparative experiments involving machine learning models—specifically leveraging versions of Python (3.8) and relevant libraries such as NLTK (3.6.3) and TensorFlow (2.5). Our findings reveal that while Syntax-Driven models exhibit lower latency in grammatical parsing tasks, Semantics-Driven models significantly outperform in semantic understanding and inference tasks by achieving p-values below 0.01 in statistical significance testing. These results point to the nuanced strengths of each approach and offer a pathway for future research in hybridizing these methodologies.