Natural-language understanding
This article needs to be updated. The reason given is: lack of discussion of recent developments related to large language models, but also no mention of older techniques like word embedding or word2vec . (February 2024) |
Natural-language understanding (NLU) or natural-language interpretation (NLI)
There is considerable commercial interest in the field because of its application to
History
The program
A year later, in 1965, Joseph Weizenbaum at MIT wrote ELIZA, an interactive program that carried on a dialogue in English on any topic, the most popular being psychotherapy. ELIZA worked by simple parsing and substitution of key words into canned phrases and Weizenbaum sidestepped the problem of giving the program a database of real-world knowledge or a rich lexicon. Yet ELIZA gained surprising popularity as a toy project and can be seen as a very early precursor to current commercial systems such as those used by Ask.com.[11]
In 1969,
In 1970,
In 1971, Terry Winograd finished writing SHRDLU for his PhD thesis at MIT. SHRDLU could understand simple English sentences in a restricted world of children's blocks to direct a robotic arm to move items. The successful demonstration of SHRDLU provided significant momentum for continued research in the field.[14][15] Winograd continued to be a major influence in the field with the publication of his book Language as a Cognitive Process.[16] At Stanford, Winograd would later advise Larry Page, who co-founded Google.
In the 1970s and 1980s, the natural language processing group at
The third millennium saw the introduction of systems using machine learning for text classification, such as the IBM
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 robots, 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 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,[22] 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,
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,[25] 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,[26] but they are still somewhat shallow. Systems that are both very broad and very deep are beyond the current state of the art.
Components and architecture
Regardless of the approach used, most natural-language-understanding systems share some common components. The system needs a
The system also needs theory from
Advanced applications of natural-language understanding also attempt to incorporate logical
The management of
See also
- Computational semantics
- Computational linguistics
- Discourse representation theory
- Deep linguistic processing
- History of natural language processing
- Information extraction
- Natural-language processing
- Natural-language programming
- Natural-language user interface
- Siri (software)
- Wolfram Alpha
- Open information extraction
- Part-of-speech tagging
- Speech recognition
Notes
- ^ Semaan, P. (2012). Natural Language Generation: An Overview. Journal of Computer Science & Research (JCSCR)-ISSN, 50-57
- ^ Roman V. Yampolskiy. Turing Test as a Defining Feature of AI-Completeness . In Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM) --In the footsteps of Alan Turing. Xin-She Yang (Ed.). pp. 3-17. (Chapter 1). Springer, London. 2013. http://cecs.louisville.edu/ry/TuringTestasaDefiningFeature04270003.pdf
- ^ Van Harmelen, Frank, Vladimir Lifschitz, and Bruce Porter, eds. Handbook of knowledge representation. Vol. 1. Elsevier, 2008.
- ^ Macherey, Klaus, Franz Josef Och, and Hermann Ney. "Natural language understanding using statistical machine translation." Seventh European Conference on Speech Communication and Technology. 2001.
- ^ Hirschman, Lynette, and Robert Gaizauskas. "Natural language question answering: the view from here." natural language engineering 7.4 (2001): 275-300.
- American Association for Artificial Intelligence Brief History of AI [1]
- Daniel Bobrow's PhD Thesis Natural Language Input for a Computer Problem Solving System.
- ISBN 1-56881-205-1page 286
- , p. 19
- ISBN 0-262-58150-7page 278
- ISBN 0-7167-0463-3pages 188-189
- ^ Roger Schank, 1969, A conceptual dependency parser for natural language Proceedings of the 1969 conference on Computational linguistics, Sång-Säby, Sweden, pages 1-3
- ^ Woods, William A (1970). "Transition Network Grammars for Natural Language Analysis". Communications of the ACM 13 (10): 591–606 [2]
- ISBN 0-415-19332-Xpage 89
- ^ Terry Winograd's SHRDLU page at Stanford SHRDLU
- ^ Winograd, Terry (1983), Language as a Cognitive Process, Addison–Wesley, Reading, MA.
- ^ Larry R. Harris, Research at the Artificial Intelligence corp. ACM SIGART Bulletin, issue 79, January 1982 [3]
- ISBN 0-89859-767-6page xiii
- ISBN 0-262-04073-5
- ^ Searle, John (23 February 2011). "Watson Doesn't Know It Won on 'Jeopardy!'". Wall Street Journal.
- ^ Brandon, John (2016-07-12). "What Natural Language Understanding tech means for chatbots". VentureBeat. Retrieved 2024-02-29.
- ISBN 3-540-73350-7
- ^ InfoWorld, Nov 13, 1989, page 144
- ^ InfoWorld, April 19, 1984, page 71
- ^ Building Working Models of Full Natural-Language Understanding in Limited Pragmatic Domains by James Mason 2010 [4]
- ISBN 1-55860-754-4page 289
- ^ 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.
- ISBN 0-415-16792-2page 209
- ISBN 0-89838-287-4
- ISBN 0-7923-8571-3
- ISBN 0-8058-2166-X
- ^ Wong, Yuk Wah, and Raymond J. Mooney. "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.
- ISBN 0-13-629478-2
- ISBN 0-201-18053-7
- ISBN 0-262-18192-4page 111
- ISBN 0-7923-6350-7
- ^ Programming with Natural Language Is Actually Going to Work—Wolfram Blog
- ^ Van Valin, Jr, Robert D. "From NLP to NLU" (PDF).
- ^ Ball, John. "multi-lingual NLU by Pat Inc". Pat.ai.