1. Application Area: Natural Language Processing
Natural communicaton
Issue: producing meaning from structure
More than symbol manipulations
“out of sight, out of mind”
“invisible idiot”
“I saw the car by the house with a chimney”
“I saw the car by the house with a flat tire”

2. NLP: A Target Behavior
Task: Given a file of data containing test results, calculate the relevant statistics about each person and display and save a table of results in sorted order based on average

3. Command Line Solution
stats < data.txt | sort –k 3,3 n > results.txt ; lpr results.txt

4. GUI and App Solution Select program based on icon location
Select run action from hierarchical menus
Specify input file in dialog box
Reorder displayed output by clicking on column header once or twice
Select “save as” and type file name in dialog box
Select print menu option and click and type until desired format is chosen

5. NLP/Intelligent Agent Solution
“<name>, I need stats run for the skills tests. Please sort them by average and display and save them into a file”

6. NLP: Context Matters What image do you have? “the prince threw a ball”
“Cinderella”
“caught”
“a cold”
“and couldn’t run fast enough to catch it”

7. NLP: Context and Meaning Subtlety
Statement: (to in-car navigational system)
“I need to get to the airport quickly”
Context: You are in the New York City area
3 airports
“quickly”
Lowest mean
Lowest standard deviation
Purpose

8. NLP: Context and Meaning Subtlety
Advance Planning
Statement: (to telephone assistant for public transportation)
“I need to get to the airport quickly and cheaply”
“What’s a [fast|quick|short] route to the airport?
“How do I get to the airport as [fast|quickly] as possible?
“Can you tell me a [fast|quick|short] route to the airport.”
“It would be nice to get to the airport quickly.

9. Traditional NLP Approach
Build parse tree of structure
Define algorithms to traverse tree and produce meaning structure
Symbolic in nature

10. Traditional NLP Approach (Semantic Grammar)
Blend parsing and meaning together
Useful in precise and limited domain
Example: Circuit Fix-it Shop:
“the switch is up”
assertion(position,switch,up,true)
“the switch is not down”
assertion(position,switch,down,false)
Issue: canonical form

11. Statistical NLP Approaches
(Normally) requires annotated corpus of data that is “correct”
Use machine learning techniques for rules for identifying structures/meaning based on features in the data
Human intelligence required for annotation, and feature identification

12. Statistical NLP: Word Sense Disambiguation
Is “bass” about a fish or about music?
What if a nearby word is?
striped
guitar
piano
sea
violin
are

13. Applicaton: Proverb Duke University probabilistic crossword solver
Set of expert modules that each try to find a solution for a given clue
Choices combined probabilistically to choose best response
85 to 95% accuracy on New York Times puzzles (1998)

14. Application: Watson
IBM computerized Jeopardy solver (deep question answering)
Lots of context
Category: Rhyme Time
Clue: It’s where Pele stores his ball
Answer: Soccer locker
Combines probabilistic and symbolic NLP

15. Watson (cont.)
Use understanding of clues to retrieve documents that may contain the answer
Expert modules probabilistically combined
2 hours CPU on a single machine

16. NLP References James Allen, Natural Language Understanding, 1995.
Daniel Jurafsky and James Martin, Speech and Language Processing, 2009.