Presentation at the Dave Waltz Symposium, held at Brandeis University, September 23, 2012. Theme: "How Dave Waltz influenced your research."

1. Waltz Questioning:
Asking the Right Question
September 23, 2012
James Pustejovsky
David Waltz Symposium
Brandeis University

2. Talk
• Personal Connection to Dave
• Waltz Questioning Algorithm
• Application Instance
• Results and Evaluation
• 😉

3. Dave’s Time at Brandeis
• Hired in 1984, part-time tenured Professor of CS.
• Started teaching AI
• Brought students to Brandeis from Illinois:
– Tony Maddox, Ph.D.
• Kept connection to Illinois:
– Jordan Pollack, Ph.D.
• Started hiring positions in AI
– Rick Alterman
– Me
• Helped found Ph.D. program at Brandeis
• Instrumental in Planning and Funding for Volen Center
• Left in 1993
• Left a culture of AI: we hired Jordan Pollack

4. Personal Connection to Dave
• Ph.D. Umass Amherst Linguistics
• Hired from UMass Amherst Postdoc in COINS
with David McDonald
• Dave helped hire me at Brandeis
– Thinking Machines was a scary option
• Dave wanted to establish AI at Brandeis while still
working at TMI
– a little security to go with the drama of a startup
• Exposed me to Example-based reasoning (EBR,
MBR)

5. Challenge as an Opportunity
• Simple rule-based NLP was failing to handle
real language data.
• Memory-based reasoning can be used to solve:
– Syntactic parsing issues (attachment, rule choice)
– Word sense disambiguation
– The frontier: story understanding, inference

6. Waltz Questioning
You just presented the conclusions of years of work, culminating
in a solution, G.
Waltz-Agent
1. Assume G is true and interesting.
2. Introduce a new problem, H, that is (arguably) more
interesting than G.
3. Establish the belief that H subsumes G, and
4. Maximize confidence that you can find connection between
G and H.
5. Solve for H.

7. Verbal Event Meaning
• Mary knows calculus. (state)
• John jogged in the park. (activity)
• Bill found a dollar. (achievement)
• Mary build a house. (accomplishment)
• Lexical Aspect and Aktionsarten

8. Changing Meaning in Context
• It was then that I knew he did it.
– (achievement)
• John jogged to the store.
– (accomplishment)
• Bill found a house in Cambridge in 3 months.
– (accomplishment)
• Mary build houses for years.
– (activity)
• Aspect Calculus  Event Structure Theory = G

9. Waltz Questioning: 1/3
1. Assume “aspect calculus” is G.
2. Introduce a new problem, H, that is (arguably)
more interesting than G:
 H = General Lexical ambiguity

10. The Problem of Lexical Ambiguity
• Homonymy: unrelated senses of a word:
– We sat on the bank of the river.
– The bank lowered its interest rate.
– Julie is the chair of the committee.
– Put four chairs at each table in the room.
• Solution: Memory-based Parsing

11. Polysemy
• Conceptually related senses of a word:
– Pick up the course book at the university store.
– I don’t agree with his recent book at all.
– We painted the door blue with white trim.
– John walked through the door.

12. How Many Meanings?
• Good
– I need a good car (cities/soccer/racing/…)
– good meal
– good knife
• Noisy
– noisy car
– noisy room
• Fast
– fast typist
– fast train
– fast highway

13. Two Types of Polysemy
• Inherent polysemy: where multiple interpretations of an
expression are available by virtue of the semantics
inherent in the expression itself.
• Selectional polysemy: where any novel interpretation of an
expression is available due to contextual influences,
namely, the type of the selecting expression.
1. a. John bought the new Obama book.
b. John doesn’t agree with the new Obama book. (inherent)
2. a. Mary left after her cigarette. (selectional)
b. I’ll call you after my coffee.

14. Waltz Questioning 2/3
1. Assume “aspect calculus” is G.
2. Introduce a new problem, H, lexical ambiguity.
3. Establish the belief that H subsumes G.
4. Maximize confidence that you can find
connection between G and H.
– Connections through enriching the representation:
• Lexical qualia structure
• Coercion and co-composition rules

15. Waltz Questioning: 3/3
1. Assume “aspect calculus” is G.
2. Introduce a new problem, H, lexical ambiguity.
3. Establish the belief that H subsumes G.
4. Maximize confidence that you can find
connection between G and H.
– Connections:
• Lexical qualia structure
• Coercion and co-composition rules
5. Solve for H.
– (Generative Lexicon Theory)

16. Challenging the Established Doctrine
• Principle of Compositionality:
– The meaning of a complex expression is
determined by its structure and the meanings of
its constituents.
• What is encoded as constituent meaning?
• What is encoded as the structure?
• Data, data, data!!!!!!
– Let the data guide you in your modeling

17. Let the Data Guide You
• Language models require:
– Thousands of KR axioms per word
– Millions of contexts per phrase/sentence
– Thousands of semantic and pragmatic features
influencing interpretation.

18. Waltz Questioning 4/3
• Dave’s legacy for NLP research:
• Solve for a better H with a better description
of the problem.