Context-Driven Natural Language Understanding Framework for Advanced Text Analytics

Abstract

NLP is now the core of modern AI system because it can analyze and comprehend human language in a more natural way. Recently, the performance of Transformer based architecture and context aware embedding based model have improved the quality of (specially for performing tasks which require fine-grained understanding). But there are still nagging problems (e.g. semantic ambiguity, complex contextual inference, multi-lingual adaptation, computational efficiency) keep popping up, and definitely they reduce the perceived reliability of NLP systems in real-world applications. We suggest a context-aware, transformer-based NLP model for intelligent text analytics, such as sentiment analysis, in this article. This approach combines a series of processes, including text preprocessing for contextual embedding, attention-guided semantic learning, and deep learning-based classification methods to improve language representation and the overall performance of text classification. In the experiments, the proposed method makes significanly better results than traditional NLP method, which is inspiring. Moreover, it can be anticipated that the framework can contribute to the realization of future Smart Communication Systems, Healthcare Analytics, Educational Platforms, and possibly even Enterprise Knowledge Management, and so on, rather straightforwardly.