From Hand-Crafted Rules to Zero-Shot Learning: A Practical History of Information Extraction

Author(s)	Mr. ayush guleria, Mr. umang bhardwaj, Dr. Shivam Sharma
Country	India
Abstract	Information Extraction (IE) has evolved from rigid rule-based systems to highly flexible zero-shot learning frameworks over the past three decades. Early IE models relied on hand-crafted linguistic rules that were domain-specific and hard to scale. The advent of statistical and supervised learning introduced adaptability but required large annotated datasets. Deep learning further improved performance through representation learning but remained data-intensive. The recent paradigm of zero-shot and few-shot learning, powered by pretrained language models like GPT and T5, enables generalization to unseen tasks with minimal supervision. This paper presents a practical history of IE’s evolution, comparing rule-based, statistical, deep learning, and zero-shot approaches. It analyzes their strengths, limitations, and trade-offs, and highlights how modern zero-shot models are transforming IE into a scalable, domain-agnostic, and cost-efficient technology for extracting structured knowledge from unstructured text.
Keywords	Information Extraction (IE), Rule-Based Systems, Statistical Learning, Deep Learning, Zero-Shot Learning, Natural Language Processing (NLP), Transfer Learning, Prompt Engineering, Knowledge Extraction
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-20
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.60759

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