Outline of natural language processing: Difference between revisions

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{{Short description|1=Overview of and topical guide to natural language processing}}

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**** A subfield of [[computational linguistics]] &ndash; interdisciplinary field dealing with the statistical or rule-based modeling of natural language from a computational perspective.

** An application of [[engineering]] &ndash; science, skill, and profession of acquiring and applying scientific, economic, social, and practical knowledge, in order to design and also build structures, machines, devices, systems, materials and processes.

*** An application of [[software engineering]] &ndash; application of a systematic, disciplined, quantifiable approach to the design, development, operation, and maintenance of software, and the study of these approaches; that is, the application of engineering to software.<ref name="BoDu04">[[Software Engineering Body of Knowledge|SWEBOK]] {{Cite book| editors editor1= Pierre Bourque and |editor2=Robert Dupuis | title = Guide to the Software Engineering Body of Knowledge - 2004 Version | publisher = [[IEEE Computer Society]] | year = 2004 | pages = 1 | isbn = 0-7695-2330-7 | url = http://www.swebok.org | authorothers = executive editors, Alain Abran, James W. Moore ; editors, Pierre Bourque, Robert Dupuis.}}</ref><ref>{{cite web

| last = ACM

| year = 2006

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* [[Maluuba]] &ndash; intelligent personal assistant for Android devices, that uses a contextual approach to search which takes into account the user's geographic ___location, contacts, and language.

* [[METAL MT]] &ndash; machine translation system developed in the 1980s at the University of Texas and at Siemens which ran on Lisp Machines.

* [[Never-Ending Language Learning]] &ndash; semantic machine learning system developed by a research team at Carnegie Mellon University, and supported by grants from DARPA, Google, and the NSF, with portions of the system running on a supercomputing cluster provided by Yahoo!.<ref name=NYT2010>{{cite news |author= |coauthorsauthors= |title=Aiming to Learn as We Do, a Machine Teaches Itself |url=https://www.nytimes.com/2010/10/05/science/05compute.html?hpw=&pagewanted=all |quote=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. |work=[[New York Times]] |date=October 4, 2010 |accessdate=2010-10-05 }}</ref> NELL was programmed by its developers to be able to identify a basic set of fundamental semantic relationships between a few hundred predefined categories of data, such as cities, companies, emotions and sports teams. Since the beginning of 2010, the Carnegie Mellon research team has been running NELL around the clock, sifting through hundreds of millions of web pages looking for connections between the information it already knows and what it finds through its search process – to make new connections in a manner that is intended to mimic the way humans learn new information.<ref>[http://rtw.ml.cmu.edu/rtw/overview Project Overview], [[Carnegie Mellon University]]. Accessed October 5, 2010.</ref>

* [[NLTK]] &ndash;

* [[Online-translator.com]] &ndash;