UNLOCKING LINGUISTIC INSIGHTS: BRIDGING FRAMENET AND NATURAL LANGUAGE THROUGH THEMATIC ROLE STRUCTURES

Аннотация

This study delves into the integration of Frame Net and natural language analysis through the innovative framework of Thematic Role Structures. By bridging the semantic world of Frame Net with the rich complexity of natural language, we unlock a wealth of linguistic insights. Thematic Role Structures offer a systematic and interpretable means of mapping verb-argument relationships, enabling enhanced information extraction, semantic parsing, and sentiment analysis. Through this interdisciplinary approach, we illuminate the potential for deeper linguistic understanding and demonstrate the applicability of Thematic Role Structures across diverse fields, including machine learning, computational linguistics, and beyond. KEYWORDS Frame Net; Thematic Role Structures; Natural Language Analysis; Linguistic Insights; Information Extraction; Semantic Parsing.