Automatic Recognition and Terminology Database Construction for Informal English Language Expressions in Online Communication

This research addresses the growing challenge of processing informal language expressions in online communication through the development of an advanced deep learning framework. The rapid evolution of digital communication has led to an unprecedented surge in nonstandard language usage, with over $60 \%$ of online content containing informal expressions that traditional natural language processing systems struggle to interpret accurately. Our study introduces a novel approach combining modified BERT architecture with specialized attention mechanisms specifically designed for informal language recognition. The framework incorporates a dynamic terminology database that adapts to emerging linguistic patterns in real-time. Through extensive experimentation on diverse online platforms, our system demonstrated remarkable improvements in recognition accuracy, achieving a $94.7 \%$ success rate, surpassing existing approaches by $2.7 \%$. The implementation of adaptive learning strategies reduced ambiguity-related errors by $40 \%$, while maintaining efficient processing speeds of $\mathbf{4 5 m s}$ per query. The system’s scalability was validated through comprehensive stress testing, consistently handling 1200 queries per second on standard hardware configurations. The research contributes significant advancements in three key areas: optimized attention mechanisms for informal language processing, dynamic terminology database construction, and efficient error handling strategies. Our findings provide valuable insights into the patterns of online communication evolution while establishing practical solutions for automated content analysis. This work lays a foundation for future developments in processing non-standard language expressions across various digital platforms.