1. NATURAL LANGUAGE PROCESSING Zachary McNellis

2. Overview  Background  Areas of NLP  How it works?  Future of NLP  References

3. 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…”

4. Background Have you used any of these?  Auto-complete  Spell-check  Did you mean…?  Trending modules

5. 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

6. Why Natural Language Processing?  The Indexed World Wide Web contains 3.68 billion pages  Search Engines  Machine Learning

7. Machine Learning  Finding statistical regularities or other patterns in the data  Clustering  System will perform well on unseen data instances

8. 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

9. Domains of NLP  What else? Medical Forensics Education Politics Marketing Businesses Government Database Management

10. How?  Linguistic Analysis  Information Extraction  Information Retrieval  Collaborative Filtering

11. Linguistic Analysis  Learn meaning of a word in context  Identify subject and predicate  Word Relations  Parts of speech  Synonyms  Antonyms  Hyponyms  Hypernyms

12. Information Extraction  Extract Information  Who?  What?  When?  Where?  Patterns  New Trend

13. Information Retrieval  Different than extraction?  Indexing to find documents relevant to the input

14. 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

15. 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

16. Issues  Accurate based on context?  Incorrect translations

17. References http://research.microsoft.com/en- us/groups/nlp http://www.ai.mit.edu/courses/6.891-nlp/ http://nlp.cs.berkeley.edu/index.shtml http://people.cs.umass.edu/~dasmith/inlp http://people.cs.jhu.edu/~jason/465/PDFSli des/lect35-future.pdf http://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.jpg http://cs-people.bu.edu/celiu/cs542/MachineLearning.jpg http://www.chinasmack.com/2010/pictures/chinglish-signs-photographed-by- nyt-der-spiegel-journalists.html http://www.noahlab.com.hk/wp-content/uploads/2012/06/nlp.jpg http://1.bp.blogspot.com/- zB7feVat6ig/UFnRHKWylKI/AAAAAAAAAIk/qOli_O9D0H0/s400/post-02- 01.jpg http://home.messiah.edu/~mg1260/www.jpg http://www.realtrafficproductions.com/Portals/4/G.B.Y.Logos.1.png https://sites.google.com/site/sergeymelderis/word.png http://www.aim.org/wp-content/uploads/2012/09/Truman-newspaper-cu.jpg ResearchPictures