henry lieberman mit media lab reasoning from (not quite) text henry lieberman (with catherine...

Henry Lieberman • MIT Media Lab

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


Big success for Reasoning with Text this week!

Yow!


For the play-by-play….


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


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


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


Textual Entailment

Yow!


Logical Reasoning:Classic example

Birds can fly.

Tweety is a bird.

Therefore… Tweety can fly.


Logical Reasoning:Not-so-classic example

Cheap apartments are rare.




Rare things are expensive.




Rare things are expensive.

Therefore… Cheap apartments are expensive.

So, exactly what was wrong with that??


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


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")


Logical vs. Commonsense knowledge

Precise Vague

Formal Natural language

Experts General public

Explicit Implicit

Consistent Possibly contradictory

Up-front organization Back-end organization


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?


Open Mind Common Sense

http://openmind.media.mit.edu


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 10-100 thousand


Open Mind Commons - Speer


Granularity

How much parsing should you do?

Stemming, Lemmatizing, Chunking, Tagging, …

Something’s lost and something’s gained

Adjustable granularity


Effect of the parser


ConceptNet relations


ConceptNet - Liu, Singh, Eslick


AnalogySpace – Speer, Havasi


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


AnalogySpace matrix


Dimensionality Reduction


Singular Value Decomposition


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


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


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


Blending - Havasi

Inference combining two AnalogySpaces

Specialized and generalized knowledge bases

Blending factor


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?


CrossBridge - Krishnamurthy


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


Example:Earth Sciences Knowledge

Collaboration with Schlumberger

Collect Earth Sciences Knowledge for intelligent search & browsing

~ 2000 assertions = 300 manual + 1600 from game

Game = 2 one-hour sessions x 10 people

350 concepts, read glossary document


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]


Knowledge collection:Common Consensus - Smith


Geology knowledge space

You can find oil where there are lizards


Luminoso – Speer, Havasi

Turnkey Opinion Analysis & Visualization platform

Constructs AnalogySpace from sets of text files


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


You can use our stuff!

http://csc.media.mit.edu


Event Networks – Dustin SmithTomorrow at 4!


ToDoGo – Dustin Smith

Yow!


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


Thanks!

Henry Lieberman

MIT Media Lab


Title

Yow!

henry lieberman mit media lab reasoning from (not quite) text henry lieberman (with catherine...

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