Design and Implementation of End-to-End Natural Language Processing Pipelines

Abstract

The evolution of Natural Language Processing (NLP) from symbolic rule-based systems to the current era of large-scale self-supervised transformer models represents one of the most significant paradigm shifts in the history of artificial intelligence. This research paper provides an exhaustive examination of the modern NLP pipeline, a modular framework that orchestrates the journey of textual data from raw acquisition to industrial-scale model serving. The analysis begins with the foundational stage of data collection and text preprocessing, evaluating the linguistic and computational trade-offs of modern tokenization strategies like Byte Pair Encoding and Word Piece over traditional word-level methods. We then provide a rigorous technical comparison of feature engineering techniques, tracing the transition from sparse statistical representations such as TFIDF to dense, contextualized embeddings that capture the nuanced polysemy of language. The architecture of the modelling phase is scrutinized, contrasting the sequential limitations of Recurrent Neural Networks and Long Short-Term Memory units with the parallelization and attention-driven capabilities of Transformers. Furthermore, the study explores the engineering complexities of model deployment, comparing the performance metrics of asynchronous frameworks like FastAPI against traditional WSGI applications in containerized production environment. Finally, the research addresses the emerging challenges of Green AI, privacy preserving NLP, and model drift, while offering a strategic outlook on future trends such as agentic systems and multi-modal foundational models. This comprehensive review serves as a definitive guide for researchers and practitioners aiming to build robust, scalable, and ethically grounded NLP systems.

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