Transformer-Based Approach to Contextual Phrasal Verb Classification

Abstract

Phrasal verbs pose a significant chal lenge in natural language understanding due to their context-dependent meanings. This study investi gates the impact of fine-tuning BERT on the task of context-aware phrasal verb disambiguation. Using a curated dataset of phrasal verbs with annotated meanings, we first evaluate a baseline model’s per formance on semantic similarity and text generation metrics. Subsequently, we fine-tune BERT on the dataset and re-assess its effectiveness. The results demonstrate consistent improvements across all met rics after fine-tuning: Cosine Similarity increased from 0.5889 to 0.6189, BLEU Score improved from 0.2570 to 0.3150, ROUGE-L rose from 0.4623 to 0.4901, Jaccard Similarity from 0.3484 to 0.3648, and METEORfrom0.3555 to 0.3607. These findings highlight that fine-tuning BERT enhances its ability to capture the nuanced meanings of phrasal verbs in context, which is critical for advancing semantic classification tasks. Future work will extend this approach to idiomatic expressions and broader lin guistic ambiguity.