Entity extraction – a comprehensive exploration

In the era of big data and information overload, the ability to extract meaningful insights from unstructured text data has become increasingly valuable. Entity extraction, a subfield of NLP, plays a pivotal role in this endeavor by identifying and classifying named entities within text, such as people, organizations, locations, and more. This process not only facilitates information retrieval and knowledge management but also enables a wide range of applications, including question-answering, sentiment analysis, and decision support systems (DSSs).

The journey of entity extraction began with simple pattern-matching and rule-based systems, which relied heavily on manually crafted rules and lexicons. These methods, while useful, lacked scalability and robustness when dealing with diverse and complex datasets.

Hence, traditionally, entity extraction has been a challenging task, requiring extensive manual effort and domain-specific...