Editing Natural language understanding (section)

==Components and architecture==
Regardless of the approach used, most NLU systems share some common components. The system needs a [[lexicon]] of the language and a [[parser]] and [[grammar]] rules to break sentences into an internal representation. The construction of a rich lexicon with a suitable [[Ontology (information science)|ontology]] requires significant effort, ''e.g.'', the [[Wordnet]] lexicon required many person-years of effort.<ref>G. A. Miller, R. Beckwith, C. D. Fellbaum, D. Gross, K. Miller. 1990. ''WordNet: An online lexical database''. Int. J. Lexicograph. 3, 4, pp. 235-244.</ref>

The system also needs theory from ''[[semantics]]'' to guide the comprehension. The interpretation capabilities of a language-understanding system depend on the semantic theory it uses. Competing semantic theories of language have specific trade-offs in their suitability as the basis of computer-automated semantic interpretation.<ref>''Using computers in linguistics: a practical guide'' by John Lawler, Helen Aristar Dry 198 {{ISBN|0-415-16792-2}} page 209</ref> These range from ''[[naive semantics]]'' or ''[[stochastic semantic analysis]]'' to the use of ''[[pragmatics]]'' to derive meaning from context.<ref>Naive semantics for natural language understanding'' by Kathleen Dahlgren 1988 {{ISBN|0-89838-287-4}}</ref><ref>''Stochastically-based semantic analysis'' by Wolfgang Minker, [[Alex Waibel]], Joseph Mariani 1999 {{ISBN|0-7923-8571-3}}</ref><ref>''Pragmatics and natural language understanding'' by Georgia M. Green 1996 {{ISBN|0-8058-2166-X}}</ref> [[Semantic parser]]s convert natural-language texts into formal meaning representations.<ref>Wong, Yuk Wah, and [[Raymond J. Mooney]]. "[http://www.aclweb.org/anthology/N06-1056 Learning for semantic parsing with statistical machine translation]." Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics. Association for Computational Linguistics, 2006.</ref>

Advanced applications of NLU also attempt to incorporate logical [[inference]] within their framework. This is generally achieved by mapping the derived meaning into a set of assertions in [[predicate logic]], then using [[logical deduction]] to arrive at conclusions. Therefore, systems based on functional languages such as [[Lisp (programming language)|Lisp]] need to include a subsystem to represent logical assertions, while logic-oriented systems such as those using the language [[Prolog]] generally rely on an extension of the built-in logical representation framework.<ref>''Natural Language Processing Prolog Programmers'' by M. Covington, 1994 {{ISBN|0-13-629478-2}}</ref><ref>''Natural language processing in Prolog'' by Gerald Gazdar, Christopher S. Mellish 1989 {{ISBN|0-201-18053-7}}</ref>

The management of [[context (language use)|context]] in NLU can present special challenges. A large variety of examples and counter examples have resulted in multiple approaches to the [[Formal semantics (natural language)|formal modeling]] of context, each with specific strengths and weaknesses.<ref>''Understanding language understanding'' by Ashwin Ram, Kenneth Moorman 1999 {{ISBN|0-262-18192-4}} page 111</ref><ref>''Formal aspects of context'' by Pierre Bonzon et al 2000 {{ISBN|0-7923-6350-7}}</ref>