1. Natural Language Processing at NYU: the Proteus Project
Ralph Grishman
September 2009

2. Proteus Project Faculty
Ralph Grishman
Satoshi Sekine
Adam Meyers

3. ‘Just the Facts’
Vast amount of information is now available on-line in text form
but getting ‘the facts’ can be very hard and slow
Where has Secretary Clinton been over the last month?
Which places on the East Coast have had swine flu outbreaks this month?
To move from search to question answering we need more than a bag of words
we need to figure out who-did-what-to-whom

4. Understanding natural language isn’t easy
The rebels strafed the car … with automatic weapons fire. … with the Minister and his deputy.
They … died instantly … were promptly arrested.
Understanding language requires a lot of knowledge.

5. How to get all this knowledge?
By hand … too expensive
Use weakly supervised learning
Give a few examples (‘seeds’)
Use very large text corpus to learn similar examples

6. Knowledge Discovery: An Example
Goal: want to keep track of all the hirings and departures of executives need to find all the ways such events are described
Method:
identify a few seed patterns
retrieve documents containing patterns
find subject-verb-object pattern with
high frequency in retrieved documents
relatively high frequency in retrieved docs vs. other docs
add pattern to seed and repeat

7. #1: pick seed pattern
Seed: < person retires >

8. #2: retrieve relevant documents
Seed: < person retires >
Fred retired.
... Harry was named president.
Maki retired.
... Yuki was named president.
Relevant documents
Other documents

9. #3: pick new pattern Seed: < person retires >
< person was named president > appears in several relevant documents
Fred retired.
... Harry was named president.
Maki retired.
... Yuki was named president.

10. #4: add new pattern to pattern set
Pattern set: < person retires >
< person was named president >

11. Results
for some event types, unsupervised learning can do as well as manual pattern development
Recall and precision as a function of number of iterations of learner:

12. Robust Learning Quality of learned patterns is uneven
ambiguity of language leads us to learn incorrect patterns
Need to identify cases of uncertainty
Potential linguistic ambiguities
With multiple classifiers using distinct features, cases where they disagree
Query user for selected uncertain examples
Weakly supervised learning + active learning robust, rapid knowledge discovery

13. For More Information Project web site Course
nlp.cs.nyu.edu
Course
G Natural Language Processing
(Spring 2010)