Outline of natural language processing

The following outline is provided as an overview of and topical guide to natural language processing:

Natural language processing computer activity in which computers are entailed to analyze, understand, alter, or generate natural language. This includes the automation of any or all linguistic forms, activities, or methods of communication, such as conversation, correspondence, reading, written composition, dictation, publishing, translation, lip reading, and so on. Natural language processing is also the name of the branch of computer science, artificial intelligence, and linguistics concerned with enabling computers to engage in communication using natural language(s) in all forms, including but not limited to speech, print, writing, and signing.

What type of thing is natural language processing?

Natural language processing can be described as all of the following:

Prerequisite technologies

The following technologies make natural language processing possible:

Subfields of natural language processing

Related fields

Natural language processing contributes to, and makes use of (the theories, tools, and methodologies from), the following fields:

Structures used in natural language processing

Processes of NLP

Applications

Component processes

Component processes of natural language understanding

Component processes of natural language generation

Natural language generation task of converting information from computer databases into readable human language.

History of natural language processing

History of natural language processing

Timeline of NLP software

Software  Year   Creator Description Reference
Georgetown experiment 1954 Georgetown University and IBM involved fully automatic translation of more than sixty Russian sentences into English.
STUDENT 1964 Daniel Bobrow could solve high school algebra word problems.[10]
ELIZA 1964 Joseph Weizenbaum a simulation of a Rogerian psychotherapist, rephrasing her (referred to as her not it) response with a few grammar rules.[11]
SHRDLU 1970 Terry Winograd a natural language system working in restricted "blocks worlds" with restricted vocabularies, worked extremely well
PARRY 1972 Kenneth Colby A chatterbot
KL-ONE 1974 Sondheimer et al. a knowledge representation system in the tradition of semantic networks and frames; it is a frame language.
MARGIE 1975 Roger Schank
TaleSpin (software) 1976 Meehan
QUALM Lehnert
LIFER/LADDER 1978 Hendrix a natural language interface to a database of information about US Navy ships.
SAM (software) 1978 Cullingford
PAM (software) 1978 Robert Wilensky
Politics (software) 1979 Carbonell
Plot Units (software) 1981 Lehnert
Jabberwacky 1982 Rollo Carpenter chatterbot with stated aim to "simulate natural human chat in an interesting, entertaining and humorous manner".
MUMBLE (software) 1982 McDonald
Racter 1983 William Chamberlain and Thomas Etter chatterbot that generated English language prose at random.
MOPTRANS 1984 Lytinen
KODIAK (software) 1986 Wilensky
Absity (software) 1987 Hirst
AeroText 1999 Lockheed Martin Originally developed for the U.S. intelligence community (Department of Defense) for information extraction & relational link analysis
Watson 2006 IBM A question answering system that won the Jeopardy! contest, defeating the best human players in February 2011.

General natural language processing concepts

Natural language processing tools

Corpora

Natural language processing toolkits

The following natural language processing toolkits are popular collections of natural language processing software. They are suites of libraries, frameworks, and applications for symbolic, statistical natural language and speech processing.

NameLanguageLicenseCreatorsWebsite
Apertium C++, Java GPL (various)
Deeplearning4j Java, Scala Apache 2.0 Adam Gibson, Skymind
DELPH-IN LISP, C++ LGPL, MIT, ... Deep Linguistic Processing with HPSG Initiative
Distinguo C++ Commercial Ultralingua Inc.
FreeLing C++ (with Java, Python, and Perl APIs) Affero GPL TALP Research Center, Universitat Politècnica de Catalunya
General Architecture for Text Engineering (GATE)Java LGPL GATE open source community
Gensim Python LGPL Radim Řehůřek
LinguaStreamJava Free for research University of Caen, France
Mallet Java Common Public License University of Massachusetts Amherst
Modular Audio Recognition Framework Java BSD The MARF Research and Development Group, Concordia University
MontyLingua Python, JavaFree for research MIT
Natural Language Toolkit (NLTK) Python Apache 2.0
Apache OpenNLP JavaApache License 2.0Online community
UIMAJava / C++ Apache 2.0 Apache
DKPro CoreJava Apache 2.0 / Varying for individual modules Technische Universität Darmstadt / Online community

Named entity recognizers

Translation software

Other software

Chatterbots

For online chatterbots with avatars, see Automated online assistant.

Chatterbot text-based conversation agent that can interact with human users through some medium, such as an instant message service. Some chatterbots are designed for specific purposes, while others converse with human users on a wide range of topics.

Classic chatterbots

General chatterbots

Instant messenger chatterbots

Natural language processing organizations

Natural language processing-related conferences

Companies involved in natural language processing

Natural language processing publications

Books

Book series

Journals

Persons influential in natural language processing

See also

External links

References

  1. "... modern science is a discovery as well as an invention. It was a discovery that nature generally acts regularly enough to be described by laws and even by mathematics; and required invention to devise the techniques, abstractions, apparatus, and organization for exhibiting the regularities and securing their law-like descriptions." —p.vii, J. L. Heilbron, (2003, editor-in-chief) The Oxford Companion to the History of Modern Science New York: Oxford University Press ISBN 0-19-511229-6
    • "science". Merriam-Webster Online Dictionary. Merriam-Webster, Inc. Retrieved 2011-10-16. 3 a: knowledge or a system of knowledge covering general truths or the operation of general laws especially as obtained and tested through scientific method b: such knowledge or such a system of knowledge concerned with the physical world and its phenomena
  2. SWEBOK executive editors, Alain Abran, James W. Moore ; editors, Pierre Bourque, Robert Dupuis. (2004). Pierre Bourque and Robert Dupuis, eds. Guide to the Software Engineering Body of Knowledge - 2004 Version. IEEE Computer Society. pp. 1–1. ISBN 0-7695-2330-7.
  3. ACM (2006). "Computing Degrees & Careers". ACM. Retrieved 2010-11-23.
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  5. Input device Computer Hope
  6. McQuail, Denis. (2005). Mcquail's Mass Communication Theory. 5th ed. London: SAGE Publications.
  7. Yucong Duan, Christophe Cruz (2011), [http //www.ijimt.org/abstract/100-E00187.htm Formalizing Semantic of Natural Language through Conceptualization from Existence]. International Journal of Innovation, Management and Technology(2011) 2 (1), pp. 37-42.
  8. McGill University, Tool Module: Chomsky’s Universal Grammar
  9. Roger Schank, 1969, A conceptual dependency parser for natural language Proceedings of the 1969 conference on Computational linguistics, Sång-Säby, Sweden pages 1-3
  10. McCorduck 2004, p. 286, Crevier 1993, pp. 76−79, Russell & Norvig 2003, p. 19
  11. McCorduck 2004, pp. 291–296, Crevier 1993, pp. 134−139
  12. "МНОГОЦЕЛЕВОЙ ЛИНГВИСТИЧЕСКИЙ ПРОЦЕССОР ЭТАП-3". Iitp.ru. Retrieved 2012-02-14.
  13. "Aiming to Learn as We Do, a Machine Teaches Itself". New York Times. October 4, 2010. Retrieved 2010-10-05. Since the start of the year, a team of researchers at Carnegie Mellon University — supported by grants from the Defense Advanced Research Projects Agency and Google, and tapping into a research supercomputing cluster provided by Yahoo — has been fine-tuning a computer system that is trying to master semantics by learning more like a human.
  14. Project Overview, Carnegie Mellon University. Accessed October 5, 2010.
  15. "Loebner Prize Contest 2013". People.exeter.ac.uk. 2013-09-14. Retrieved 2013-12-02.
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  18. "ActiveBuddy Introduces Software to Create and Deploy Interactive Agents for Text Messaging; ActiveBuddy Developer Site Now Open: www.BuddyScript.com". Business Wire. 2002-07-15. Retrieved 2014-01-16.
  19. Lenzo, Kevin (Summer 1998). "Infobots and Purl". The Perl Journal. 3 (2). Retrieved 2010-07-26.
  20. Wermter, Stephan; Ellen Riloff; Gabriele Scheler (1996). Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. Springer.
  21. Jurafsky, Dan; James H. Martin (2008). Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2nd ed.). Upper Saddle River (N.J.): Prentice Hall. p. 2.
  22. "SEM1A5 - Part 1 - A brief history of NLP". Retrieved 2010-06-25.
  23. Roger Schank, 1969, A conceptual dependency parser for natural language Proceedings of the 1969 conference on Computational linguistics, Sång-Säby, Sweden, pages 1-3
  24. 1 2 Ibrahim, Amr Helmy. 2002. "Maurice Gross (1934-2001). À la mémoire de Maurice Gross". Hermès 34.
  25. Dougherty, Ray. 2001. Maurice Gross Memorial Letter.
  26. Lamiroy, Béatrice. 2003. " In memoriam Maurice Gross ", Travaux de linguistique 46:1, pp. 145-158.
  27. http://blog.wolfram.com/2010/11/16/programming-with-natural-language-is-actually-going-to-work/
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