Information extraction (IE) is the automated retrieval of specific information related to a selected topic from a body or bodies of text.
Information extraction tools make it possible to pull information from text documents, databases, websites or multiple sources. IE may extract info from unstructured, semi-structured or structured, machine-readable text. Usually, however, IE is used in natural language processing (NLP) to extract structured from unstructured text.
Information extraction depends on named entity recognition (NER), a sub-tool used to find targeted information to extract. NER recognizes entities first as one of several categories such as location (LOC), persons (PER) or organizations (ORG). Once the information category is recognized, an information extraction utility extracts the named entity’s related information and constructs a machine-readable document from it, which algorithms can further process to extract meaning. IE finds meaning by way of other subtasks including co-reference resolution, relationship extraction, language and vocabulary analysis and sometimes audio extraction.
IE dates back to the early days of Natural Language Processing of the 1970’s. JASPER is a system for IE that for Reuters by Carnegie Melon University is an early example. Current efforts in multimedia document processing in IE include automatic annotation and content recognition and extraction from images and video could be seen as IE as well.
Because of the complexity of language, high-quality IE is a challenging task for artificial intelligence (AI) systems.