Information extraction
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 <math>MergerBetween(company_1, company_2, date)</math>, 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 database|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 phrase]]s that refer to the same object. For example, [[Anaphora (linguistics)|anaphora]] is a type of coreference.
* [[Terminology extraction]]: finding the relevant terms for a given [[text corpus|corpus]]
* Relation 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.")

==See also==
* [[Concept mining]]
* [[HAREM]], a Portuguese named entity recognition contest
* [[General Architecture for Text Engineering]] "General Architecture for Text Engineering", which is bundled with a free Information Extraction system

== External links==
{{external links}}
* [http://www.opencalais.com OpenCalais] Automated information extraction tool from [[Reuters]] 
* [http://www.itl.nist.gov/iaui/894.02/related_projects/muc/ MUC]
* [http://projects.ldc.upenn.edu/ace/ ACE] (LDC)
* [http://www.itl.nist.gov/iad/894.01/tests/ace/ ACE] (NIST)
* [http://lcl2.di.uniroma1.it TermExtractor]
* [http://labs.translated.net/terminology-extraction/ TermFinder], online terminology extractor for EN, FR & IT - [[web application]]
* [http://www.cs.washington.edu/research/textrunner/ TextRunner] Part of the KnowItAll Project of the [http://turing.cs.washington.edu/ Turing Center] at the [[University of Washington]]
* [http://gate.ac.uk/ GATE]

[[Category:Natural language processing]]
[[Category:Artificial intelligence]]

[[cs:Extrakce informací]]
[[de:Informationsextraktion]]
[[el:Εξαγωγή πληροφοριών]]
[[es:Extracción de la información]]
[[eu:Informazio erauzketa]]
[[ja:情報抽出]]
[[ru:Извлечение информации]]
[[zh:信息抽取]]