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Background Computers are dumb Enabling computers to learn from human input Artificial Intelligence? Machine Learning/Data Mining What is it?Language? “Haec erew qudlekr madscna kelrergko lkjaspoiwer…”
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NATURAL LANGUAGE PROCESSING
Zachary McNellis
Overview Background Areas of NLP How it works? Future of NLP References
Background
Computers are dumb Enabling computers to
learn from human input Artificial Intelligence? Machine Learning/Data
Mining
What is it? Language? “Haec erew qudlekr
madscna kelrergko lkjaspoiwer…”
BackgroundHave you used any of these? Auto-complete Spell-check Did you mean…? Trending modules
Background (Again) How can you understand what the user
wants? Natural Language Understanding
Taking text and determine its meaning Natural Language Generation
Take some representation of what you want to say and express it in a natural language
Why Natural Language Processing? The Indexed World
Wide Web contains 3.68 billion pages
Search Engines Machine Learning
Machine Learning Finding statistical
regularities or other patterns in the data Clustering
System will perform well on unseen data instances
Areas of NLP Information Extraction
Classify text into fixed categories
Index and search large texts Machine Translation
Text to Text Text to Speech Speech to Text Speech to Speech
Advanced Text Editors
Speech understanding Collaborative Filtering Sentiment Analysis
Good or bad? Automatic
summarization Condense a novel into a
page
Domains of NLP What else?
Medical
Forensics
Education
Politics
Marketing
Businesses
Government
Database Management
How? Linguistic Analysis Information
Extraction Information
Retrieval Collaborative
Filtering
Linguistic Analysis Learn meaning of a word in
context Identify subject and
predicate Word Relations
Parts of speech Synonyms Antonyms Hyponyms Hypernyms
Information Extraction Extract Information
Who? What? When? Where?
Patterns New Trend
Information Retrieval Different than
extraction? Indexing to find
documents relevant to the input
Collaborative Filtering Given a set of users
and items, provide recommendations to the current user of the system (Amazon) User-based filtering Item-based filtering
Future of NLP Text data
Natural Language Generation Flight(Charleston, Atlanta, 2,
$300, 3pm, 5pm) “Two flights from Charleston…”
Images Optical character recognition
Video Audio Issues
Issues Accurate based on
context? Incorrect
translations
References
http://research.microsoft.com/en-us/groups/nlphttp://www.ai.mit.edu/courses/6.891-nlp/http://nlp.cs.berkeley.edu/index.shtmlhttp://people.cs.umass.edu/~dasmith/inlphttp://people.cs.jhu.edu/~jason/465/PDFSlides/lect35-future.pdfhttp://www.worldwidewebsize.com/http://www.wisegeek.com/what-is-natural-language-processing.htm
http://www.impermium.com/blog/wp-content/uploads/2013/03/Machine-Learning-Smaller-860x1024.jpghttp://cs-people.bu.edu/celiu/cs542/MachineLearning.jpghttp://www.chinasmack.com/2010/pictures/chinglish-signs-photographed-by-nyt-der-spiegel-journalists.htmlhttp://www.noahlab.com.hk/wp-content/uploads/2012/06/nlp.jpghttp://1.bp.blogspot.com/-zB7feVat6ig/UFnRHKWylKI/AAAAAAAAAIk/qOli_O9D0H0/s400/post-02-01.jpghttp://home.messiah.edu/~mg1260/www.jpghttp://www.realtrafficproductions.com/Portals/4/G.B.Y.Logos.1.pnghttps://sites.google.com/site/sergeymelderis/word.pnghttp://www.aim.org/wp-content/uploads/2012/09/Truman-newspaper-cu.jpg
Research Pictures