A Review On Semantic Role Labelling For Indian Languages

Semantics means the process of extracting precise meaning from text or sentences. Semantic Role Labelling (SRL) plays a crucial role in Natural Language Processing by providing insights into the underlying meaning of sentences. SRL involves assigning generic labels or roles to words within a sentence, indicating their respective semantic roles. It helps in tasks such as information extraction, question answering, educational systems, sentiment analysis, and machine translation by identifying the roles of different words in a sentence and their relationships with each other. This process enables computers to better understand and process human language, leading to more accurate and effective language understanding. SRL also powers AI-driven educational tools like intelligent tutoring systems, personalized learning, and automated assessments, making education more adaptive and effective. In this paper, we are giving a brief review of SRL system developed for different languages. This literature review focuses on key aspects of SRL like techniques used, datasets used, languages for which SRL is developed, and accuracy. Our future work is inclined towards SRL for Hindi, so our review focuses on SRL developed for Hindi, along with other Indian Languages like Urdu, Malayalam and Tamil.