Text analytics

From Wikipedia, the free encyclopedia

Text analytics is a process of information extraction whose goal is to automatically extract structured or semistructured information from unstructured machine-readable documents.

A typical application is to scan a set of documents written in a natural language and populate a database or search index with the information extracted. Current approaches to text analytics use natural language processing techniques that focus on specialized domains.

Typical subtasks 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.

[edit] See also

• Noisy text analytics
• Information extraction
• Computational linguistics
• Natural language processing
• Named entity recognition
• Text mining

[edit] External links

• 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)

[edit] Commercial Software and Applications

• TEMIS - TEMIS is a software editor providing innovative Information Discovery solutions to serve the Information Intelligence needs of business corporations.