Designing a Prototype Platform for Real-Time Event Extraction: A Scalable Natural Language Processing and Data Mining Approach

Abstract

In this paper, we present a modular, high-performance prototype platform for real-time event extraction, designed to address key challenges in processing large volumes of unstructured data across applications like crisis management, social media monitoring and news aggregation. The prototype integrates advanced natural language processing (NLP) techniques (Term Frequency–Inverse Document Frequency (TF-IDF), Latent Semantic Indexing (LSI), Named Entity Recognition (NER)) with data mining strategies to improve precision in relevance scoring, clustering and entity extraction. The platform is designed to handle real-time constraints in an efficient manner, by combining TF-IDF, LSI and NER into a hybrid pipeline. Unlike the transformer-based architectures that often struggle with latency, our prototype is scalable and flexible enough to support various domains like disaster management and social media monitoring. The initial quantitative and qualitative evaluations demonstrate the platform’s efficiency, accuracy, scalability, and are validated by metrics like F1-score, response time, and user satisfaction. Its design has a balance between fast computation and precise semantic analysis, and this can make it effective for applications that necessitate rapid processing. This prototype offers a robust foundation for high-frequency data processing, adaptable and scalable for real-time scenarios. In our future work, we will further explore contextual understanding, scalability through microservices and cross-platform data fusion for expanded event coverage.