Natural Language Processing
Ellen Back, LIS489, Spring 2015
Natural Language Processing (NLP) is the
branch of computer science dedicated to
creating systems that allow computers to
communicate with people using everyday
language.
Natural language is very ambiguous and
must be disambiguated.
For example….

Ban on Nude Dancing on Governor's Desk

Iraqi Head Seeks Arms

Juvenile Court to Try Shooting Defendant

TeacherStrikesIdleKids

StolenPaintingFoundbyTree

Local High School Dropouts Cut in Half

Red Tape Holds Up New Bridges

Clinton Wins on Budget, but More Lies Ahead

Hospitals Are Sued by 7 Foot Doctors

Kids Make Nutritious Snacks
 NLP goals can be far-reaching, that computers can reason about and
understand any piece of text, or more down-to-earth such as context
sensitive spelling correction.
 Context spelling correction example: Chocolate chip cookies are my
favorite desert.
 Using NLP, IBM Watson Beats Human Champions at Jeopardy!
 https://www.youtube.com/watch?v=_Xcmh1LQB9I
 https://www.youtube.com/watch?v=DywO4zksfXw
 http://www.ibm.com/smarterplanet/us/en/ibmwatson/what-is-watson.html
 Historically, both logical and probabilistic methods have found wide application in
NLP.
 A natural language parser is a program that determines the grammatical structure
of sentences, for instance, which groups of words go together (as "phrases") and
which words are the subject or object of a verb. Probabilistic parsers use
knowledge of language gleamed from hand-parsed sentences to try to produce
the most likely analysis of new sentences. These statistical parsers still make
some errors, but commonly work rather well. Their development was one of the
most significant breakthroughs in natural language processing in the 1990s.
 Juvenile Court to Try Shooting Defendant
 You can try out the Stanford parser online.
 The Stanford NLP Group works on algorithms that allow computers to
process and understand human languages.
 Their work ranges from basic research in computational linguistics to key
applications in human language technology, and covers areas such as
sentence understanding, machine translation, probabilistic parsing and
tagging, biomedical information extraction, grammar induction, word sense
disambiguation, automatic question answering, and text to 3D scene
generation.
 Birthplace of Siri: voice recognition + sentence parsing
 https://www.youtube.com/watch?v=nfoudtpBV68
.
 At Microsoft the NLP Research team uses a mix of knowledge-engineered
and statistical/machine-learning techniques to disambiguate and respond
to natural language input.
 Their work is significant for applications like text critiquing, information
retrieval, question answering, summarization, gaming, and translation. The
grammar checkers in Office for English, French, German, and Spanish are
products of their research; Encarta employs their technology to retrieve
answers to user questions; Intellishrink uses natural language technology
to compress cellphone messages; Microsoft Product Support uses machine
translation software to translate the Microsoft Knowledge Base into other
languages.
 Programmers, computers and users all benefit from Natural Language
Processing. To this end, companies and universities are constantly trying to
improve the human-computer interaction.
 For more information about Microsoft and Stanford Research:
 http://research.microsoft.com/en-us/groups/nlp/
 http://nlp.stanford.edu/
 For general information about NLP:
 http://see.stanford.edu/see/lecturelist.aspx?coll=63480b48-8819-4efd8412-263f1a472f5a (Wonderful course from Stanford with links to video
lectures and transcripts)
 http://www.cs.utexas.edu/~mooney/cs388/
 http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6864-advanced-natural-language-processing-fall-2005/ (Because you know
you want the advanced level from MIT! )
 Journals such as Computational Linguistics and Computer Speech &
Language will provide up-to-date information in the field.
Sources
 Artificial Intelligence, Natural Language Processing. (n.d.) Engineering. Stanford
University. Retrieved from
http://see.stanford.edu/see/lecturelist.aspx?coll=63480b48-8819-4efd-8412263f1a472f5a
 Intro to NLP. (n.d.). The University of Washington. Retrieved from
http://courses.cs.washington.edu/courses/csep573/12au/lectures/19-nlp.pdf
 Natural Language Processing. (2015). Research. Microsoft. Retrieved from
http://research.microsoft.com/en-us/groups/nlp/
 Natural Language Processing. (n.d.). The University of Texas. Retrieved from
http://www.cs.utexas.edu/~mooney/cs388/
 The Stanford NLP Group. (n.d.). Stanford University Natural Language
Processing. Retrieved from http://nlp.stanford.edu/