Editing Natural language understanding (section)

==Scope and context==
The umbrella term "natural language understanding" can be applied to a diverse set of computer applications, ranging from small, relatively simple tasks such as short commands issued to [[robot]]s, to highly complex endeavors such as the full comprehension of newspaper articles or poetry passages. Many real-world applications fall between the two extremes, for instance [[Document classification|text classification]] for the automatic analysis of emails and their routing to a suitable department in a corporation does not require an in-depth understanding of the text,<ref>''An approach to hierarchical email categorization'' by Peifeng Li et al. in ''Natural language processing and information systems'' edited by Zoubida Kedad, Nadira Lammari 2007 {{ISBN|3-540-73350-7}}</ref> but needs to deal with a much larger vocabulary and more diverse syntax than the management of simple queries to database tables with fixed schemata.

Throughout the years various attempts at processing natural language or ''English-like'' sentences presented to computers have taken place at varying degrees of complexity. Some attempts have not resulted in systems with deep understanding, but have helped overall system usability. For example, [[Wayne Ratliff]] originally developed the ''Vulcan'' program with an English-like syntax to mimic the English speaking computer in [[Star Trek]]. Vulcan later became the [[dBase]] system whose easy-to-use syntax effectively launched the personal computer database industry.<ref>[[InfoWorld]], Nov 13, 1989, page 144</ref><ref>[[InfoWorld]], April 19, 1984, page 71</ref> Systems with an easy to use or English-like syntax are, however, quite distinct from systems that use a rich [[lexicon]] and include an internal [[knowledge representation and reasoning|representation]] (often as [[first order logic]]) of the semantics of natural language sentences.

Hence the breadth and depth of "understanding" aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with. The "breadth" of a system is measured by the sizes of its vocabulary and grammar. The "depth" is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, ''English-like'' command interpreters require minimal complexity, but have a small range of applications. Narrow but deep systems explore and model mechanisms of understanding,<ref>''Building Working Models of Full Natural-Language Understanding in Limited Pragmatic Domains'' by James Mason 2010 [http://www.yorku.ca/jmason/UnderstandingEnglishInLimitedPragmaticDomains.html]</ref> but they still have limited application. Systems that attempt to understand the contents of a document such as a news release beyond simple keyword matching and to judge its suitability for a user are broader and require significant complexity,<ref>''Mining the Web: discovering knowledge from hypertext data'' by Soumen Chakrabarti 2002 {{ISBN|1-55860-754-4}} page 289</ref> but they are still somewhat shallow. Systems that are both very broad and very deep are beyond the current state of the art.