Information Extraction (IE) is the process of automatically extracting pertinent information from unstructured or semi-structured data, and it typically involves the analysis of human language text through natural language processing (NLP). Rules-based methods (RBM), Supervised-learning-based methods, and Unsupervised-Learning-based methods are the three basic methods used by the IE system. This work aims to explore, analyze the various approaches, and illustrate the difficulties encountered while using textual data in different forms, domains, and sizes of datasets from a preexisting information extraction using various categories of IE methods. This study presents an analytical study of various approaches to different information extraction methods used to analysis of textual data.
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