1. Reasoning From (Not Quite) Text
Henry Lieberman
(with Catherine Havasi, Rob Speer, Ken Arnold, Dustin Smith)
MIT Media Lab & Mind-Machine Project
Cambridge, Mass. USA

2. Big success for Reasoning with Text this week!
Yow!

3. For the play-by-play….

4. We need better mechanisms for reasoning!
Clue: It was this anatomical oddity of US gymnast George Eyser.... Ken Jennings' answer: Missing a hand (wrong) Watson's answer: leg (wrong) Correct answer: Missing a leg

5. Turing’s Dream & Knowledge Challenge - Schubert
Natural language is a pretty damn good knowledge representation language Has capabilities that formal KR doesn’t Resist the urge to “simplify so the computer can understand it” Don’t be so afraid of the Ambiguity bogeyman

6. Capabilities of Natural Language Representations
Generalized Quantifiers Event/Situation Reference Modification (of Predicates & Sentences) Reification (of Predicates & Sentences) Metric/Comparative Attributes Uncertainty & Genericity Metalinguistic Capabilities

7. Textual Entailment
Yow!

8. Logical Reasoning: Classic example
Birds can fly.
Tweety is a bird.
Therefore… Tweety can fly.

9. Logical Reasoning: Not-so-classic example
Cheap apartments are rare.

10. Logical Reasoning: Not-so-classic example
Cheap apartments are rare.
Rare things are expensive.

11. Logical Reasoning: Not-so-classic example
Cheap apartments are rare.
Rare things are expensive.
Therefore… Cheap apartments are expensive.
So, exactly what was wrong with that??

12. Yeah, what's wrong with that?
Logicians say:
Not the same sense of "rare", "expensive", etc.
I say:
Maybe, but punts the problem of translating language/Commonsense to logic
Logic is about possible inference; Common Sense is about plausible inference

13. Not so interested in absolute truth as we are in…
Plausibility (not necessarily Probability)
Similarity
Analogy
Relevance
Computing "intangible" qualities (affect, point of view, connotation, overall "sense")

14. Logical vs. Commonsense knowledge
Precise Vague
Formal Natural language
Experts General public
Explicit Implicit
Consistent Possibly contradictory
Up-front organization Back-end organization

15. Logical vs. Statistical Reasoning
Big debate, much hot air We need to fill in the gap between them Word occurrences are weak evidence Symbolic expressions much stronger But how do you combine lots of them?

16. Open Mind Common Sense

17. Open Mind Common Sense “The Wikipedia version of Cyc” since 2000
1 Million English statements, other languages
How much Commonsense does an average person know?
1 human lifetime = 3 billion seconds
Less than a billion - Maybe 100 million
How much domain knowledge does a single expert know?
Less than a million - Maybe thousand

18. Open Mind Commons - Speer

19. Granularity
How much parsing should you do? Stemming, Lemmatizing, Chunking, Tagging, … Something’s lost and something’s gained Adjustable granularity

20. Effect of the parser

21. ConceptNet relations

22. ConceptNet - Liu, Singh, Eslick

23. AnalogySpace – Speer, Havasi

24. What AnalogySpace can do
It can generalize from sparsely-collected knowledge
It can identify the most important dimensions in a knowledge space
It can classify concepts along those dimensions
It can create ad-hoc categories (and classify accordingly)
It can confirm or question existing knowledge

25. AnalogySpace matrix

26. Dimensionality Reduction

27. Singular Value Decomposition

28. Traditional Logical Inference
Inferences goes from True assertion -> True assertion via Inference Rules Good news: Very precise and reliable Bad news: Proof search blows up exponentially Requires precise definitions and assertions GIGO

29. AnalogySpace Inference
All possible assertions put in a (big, sparse) box You can rearrange the box along semantic axes Good news: Computationally efficient Tolerant of imprecision, contradiction, disagreement… Stronger than statistical inference Bad news: Can’t be guaranteed to be very precise

30. Not-so-Common Sense
Use Common Sense tools & methodology, but knowledge only common to a small group
Collect knowledge from natural language sources
Collect knowledge from games
Collect knowledge from existing DBs, Ontologies, ..
"Blend" with general Commonsense knowledge
-> AnalogySpace for specific domain

31. Blending - Havasi Inference combining two AnalogySpaces
Specialized and generalized knowledge bases
Blending factor

32. CrossBridge - Krishnamurthy
AnalogySpace-based technique for Structure Mapping analogy Indexes small networks of concepts & assertions Can do Case-Based Reasoning Electricity flows through Wires -> Water flows through Pipes, or Light flows through Fiber-Optic Cables?

33. CrossBridge - Krishnamurthy

34. Applications in Interface Agents
Predictive typing, Speech recognition
Storytelling with Media Libraries
Detection and mitigation of online bullying
Opinion Analysis
Goal-oriented interfaces for Consumer Electronics
Mobile to-do lists, location-aware context-sensitive maps
Translation, language learning & multi-lingual communication
Help and customer service
Recommendation systems, scenario-based recommendation
Programming and code sharing in natural language
… and more

35. Example: Earth Sciences Knowledge
Collaboration with Schlumberger
Collect Earth Sciences Knowledge for intelligent search & browsing
~ 2000 assertions = 300 manual from game
Game = 2 one-hour sessions x 10 people
350 concepts, read glossary document

36. Geology sentences Petroleum is a mixture of hydrocarbons. [IsA]
Air gun array is used for seismic surveying offshore. [UsedFor]
A seismic survey is a measure of seismic-wave travel. [Measures]
A wildcat is an exploration well drilled in an unproven area. [IsA]
You would drill an exploration well because you want to determine whether hydrocarbons are present. [MotivatedByGoal]

37. Knowledge collection: Common Consensus - Smith

38. Knowledge collection: Common Consensus - Smith

39. Geology knowledge space
You can find oil where there are lizards

40. Luminoso – Speer, Havasi
Turnkey Opinion Analysis & Visualization platform Constructs AnalogySpace from sets of text files

41. Opinions of software
When people talk about the mechanics of using software, that means they don't like it
When people talk about what they want to do with software, that means they like it

42. You can use our stuff!

43. Event Networks – Dustin Smith Tomorrow at 4!

44. ToDoGo – Dustin Smith
Yow!

45. Conclusion
There’s been a controversy between logical and statistical reasoning We need to fill in the gap Symbolic representations as source “Do the math” to combine large numbers of them New thinking about Commonsense reasoning

46. Thanks!
Henry Lieberman MIT Media Lab

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