natural language processing (NLP) definition


The development of NLP applications is challenging because computers traditionally require humans to “speak” to them in a programming language that is precise, unambiguous and highly structured or, perhaps through a limited number of clearly-enunciated voice commands. Human speech, however, is not always precise -- it is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context.

Current approaches to NLP are based on machine learning, a type of artificial intelligence that examines and uses patterns in data to improve a program's own understanding. Most of the research being done on natural language processing revolves around search, especially enterprise search.  

Common NLP tasks in software programs today include:

The advantage of natural language processing can be seen when considering the following two statements: "Cloud computing insurance should be part of every service level agreement" and "A good SLA ensures an easier night's sleep -- even in the cloud." If you use national language processing for search, the program will recognise that cloud computing is an entity, that cloud is an abbreviated form of cloud computing and that SLA is an industry acronym for service level agreement.

The ultimate goal of NLP is to do away with computer programming languages altogether.  Instead of specialized languages such as Java or Ruby or C, there would only be "human."

See also: opinion mining, association rules, IBM Watson supercomputer

This was first published in May 2011

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