In natural language processing, information extraction (IE) is a type of information retrieval whose goal is to automatically extract structured information, i.e. categorized and contextually and semantically well-defined data from a certain domain, from unstructured machine-readable documents. An example of information extraction is the extraction of instances of corporate mergers, more formally MergerBetween(company₁,company₂,date), from an online news sentence such as: "Yesterday, New-York based Foo Inc. announced their acquisition of Bar Corp." A broad goal of IE is to allow computation to be done on the previously unstructured data. A more specific goal is to allow logical reasoning to draw inferences based on the logical content of the input data.

The significance of IE is determined by the growing amount of information available in unstructured (i.e. without metadata) form, for instance on the Internet. This knowledge can be made more accessible by means of transformation into relational form, or by marking-up with XML tags. An intelligent agent monitoring a news data feed requires IE to transform unstructured data into something that can be reasoned with.

A typical application of IE is to scan a set of documents written in a natural language and populate a database with the information extracted. Current approaches to IE use natural language processing techniques that focus on very restricted domains. For example, the Message Understanding Conference (MUC) is a competition-based conference that focused on the following domains in the past:

• MUC-1 (1987), MUC-2 (1989): Naval operations messages.
• MUC-3 (1991), MUC-4 (1992): Terrorism in Latin American countries.
• MUC-5 (1993): Joint ventures and microelectronics domain.
• MUC-6 (1995): News articles on management changes.
• MUC-7 (1998): Satellite launch reports.

Natural Language texts may need to use some form of a Text simplification to create a more easily machine readable text to extract the sentences.

Typical subtasks of IE are:

• Named Entity Recognition: recognition of entity names (for people and organizations), place names, temporal expressions, and certain types of numerical expressions.
• Coreference: identification chains of noun phrases that refer to the same object. For example, anaphora is a type of coreference.
• Terminology extraction: finding the relevant terms for a given corpus
• Relationship Extraction: identification of relations between entities, such as:
  • PERSON works for ORGANIZATION (extracted from the sentence "Bill works for IBM.")
  • PERSON located in LOCATION (extracted from the sentence "Bill is in France.")