Abstract
Information Extraction (IE) is a fundamental task in Natural Language Processing (NLP) and Computer Vision, aimed at automatically extracting structured information from unstructured data sources such as text, images, and videos. This paper provides a comprehensive survey of various IE techniques, focusing on Named Entity Recognition (NER), Relation Extraction (RE), and Opinion Classification. We discuss rule-based, unsupervised, supervised, and deep learning approaches for NER, highlighting their advantages and limitations. Additionally, we explore the role of IE in diverse applications, including scholarly literature databases, business intelligence, healthcare, patent analysis, and customer care. Furthermore, we examine IE methods applied to images and videos, covering visual relationship detection, optical character recognition (OCR), and automatic video summarization. The paper also addresses challenges such as domain adaptation, ambiguity, data privacy, and computational efficiency. Finally, we outline future research directions, emphasizing the integration of multimodal IE, advancements in deep learning, and real-time processing.