In the digital era, sentence interpretation is crucial for understanding the
meaning of sentences. As a key component of Natural Language Processing (NLP), it
helps identify relationships between words and determine their roles within a sentence.
Semantic Role Classification (SRC) assigns semantic roles to different actions in a
sentence, enabling deeper language comprehension. This study analyses various SRC
techniques, with a particular focus on transformer models. It provides a summary of
existing SRC methods, highlighting their advantages and incorporating a Continuous
Integration/Continuous Delivery (CI/CD) pipeline for seamless deployment. The
effectiveness of the proposed approach is evaluated based on the accuracy achieved.
Keywords: Semantic Role Classification, Sentence Interpretation, Natural Language Processing, Transformer Models, Machine Learning, CICD Pipeline.
