text understanding topic - ...from plain text to logic

1. From Text to Reasoning
Marko Grobelnik
Jozef Stefan Institute / Cycorp Europe, Slovenia
SWANK Workshop, Stanford, Apr 16th 2014Thanks to Michael Witbrock, Janez Starc, Luka Bradesko, Blaz Fortuna

2. Reflection on what should be the goal of
NLP
• The (mostly) forgotten long term aim of NLP is to understand the text
• …and not so much ‘processing’ itself (as NLP suggests)
• The curse of shallow solutions working well enough for too many
problems, made people (and researchers) happy for too long
• …as much as information retrieval and text mining are useful, they delayed
development of “text understanding”

3. Language vs. World
• …if we agree with the above statement, then at this point in time, we
have ‘language’, but the ‘world’ is more or less missing
• So – so what a ‘world’ or ‘world model’ could be?

4. CYC KNOWLEDGE BASE
Thing
Universe
isa
isa
Celestial
Body
isa
located in
Planet
subclass
Earth
isa
Animal
isa
Human
subclas
s
Physics
Money
Mathematics
Chemistry
Time
Learning
FoodVehicles
Event
Education
School
Language
LoveEmotions Going for a
walk
Death
Cat
Euro
Working
Words
Driving
RainStabbing someone
Nature
Tree
Hatred
Fear
Physics
Time
Learning
Vehicles
Event
Education
School
Emotions
Going for a
walk
Death
Cat
EuroWords
Driving
Rain
Stabbing someone
Nature
Tree
Hatred
Fear
Planet
Earth
isa
Human
Physics
Money
Mathematics
Chemistry
Time
Learning
FoodVehicles
Event
Education
Languag
e LoveEmotions Going for a
walk
Cat
Euro
Working
Words
Driving Rain
Tree
Hatred
Fear
Learning
Vehicles
Event
Education
School
Emotions
Euro
Driving
Stabbing someone
Hatred
Fear
Creating a World Model (top-down approach -
Cyc)

5. Model of the world…
• …beyond surface knowledge
• …to interconnect contextualized fragments
Why?
• To make reasoning capable of connecting
isolated fragments of knowledge
• To derive new knowledge beyond
materialized factual knowledge
World model
Top-down KA
Bottom-up KA
Multimodal data
Why we need a
World model?

6. Disambiguation with
a
world model
(CycKB)World model used as a set of common-sense semantic
constraints to disambiguate text

7. One of the challenges for the future: Micro-reading
• It is “easier” to understand millions of documents than one document
• …reading and understanding a single document is micro-reading
• The following experiment is on how much knowledge we can extract
from individual documents
• …extraction is in a form of first order inferentially productive Cyc logic
• …allowing us full reasoning to identify new facts
• …minimizing human involvement, optimizing precision and recall
Document Assertions Reasoning Dialogue

8. Example of text and extracted Cyc assertions
(1/2)
Automatically Extracted Assertions:
• (isa ?V1 ProsecutingEvent)
• (agent ?V1 RudyGiuliani)
• (genls Entity Agent)
• (isa RudyGiuliani Agent)
• (isa RudyGiuliani Entity)
• (isa ?V3 OrganizingEvent)
• (patient ?V3 (IntersectionFn
OrganizedCrime WallStreet))
• (isa (IntersectionFn OrganizedCrime
WallStreet) Patient)
• (genls Entity Patient)
• (isa OrganizedCrime Patient)
• (isa OrganizedCrime Entity)
• (isa WallStreet Patient)
• (isa WallStreet Entity)
Sentence:
He prosecuted a number of high-profile cases, including ones
against organized crime and Wall_Street financiers.

9. Example of text and extracted Cyc assertions
(2/2)
Automatically Extracted Assertions:
• (isa ?V1 SubstitutingEvent)
• (temporal ?V1 Lincoln)
• (genls Entity Agent)
• (isa Lincoln Agent)
• (genls Person Entity)
• (isa Lincoln Entity)
• (isa Lincoln Person)
• (isa ?V3 SucceedingEvent)
• (temporal ?V3 Grant)
• (isa Grant Agent)
• (isa Grant Entity)
• (isa Grant Person)
Sentence:
Each time a general failed, Lincoln substituted another
until finally Grant succeeded in 1865.

10. Reasoning on extracted assertions (Cyc)
Query:
(and
(isa ?Per Person)
(birthDate ?Per ?BD)
(occursBefore ?BD WorldWarII)
(thereExistsAtLeast 2 ?Role
(lifeRole ?Per ?Role)
(roleInIndustry ?Role FilmIndustry)
)
)
Answers:
Sir Derek_George_Jacobi
Sir Alexander_Korda
Victor Lonzo_Fleming
John_Francis_Junkin
Cornel_Wilde
George_Stevens
Bertrand_Blier
NL Query:
People born before World War II who had at least two roles in the film industry KB?

11. Knowledge Capture Knowledge Use
Rule:
(implies (and
(isa ?VENUE FoodTruck-Organization)
(lastVenue ?USER ?VENUE)
(suggestionsForCuriousCatQuestionType FoodTruckSecondaryTypeOfPlace-
CuriousCatQuestion ?SUGGESTIONLIST))
(curiousCatWantsToAskUser ?USER
(secondaryTypeOfPlace ?VENUE FoodTruck-Organization ?TYPE) ?SUGGESTIONLIST))
Witbrock, M., Bradeško, L., 2013,
Conversational Computation in
Michelucci, Pietro (Ed.)
Handbook of Human Computation,
531-543.
Intelligent
SIRI:
http://curiouscat.cc/

12. Some of the AI challenges for next years
• Background knowledge in a form of a World Model
• …to have knowledge contextualized
• Representing and scalable reasoning knowledge with
operational soft logic
• …to decrease brittleness of logic and increase scale
• Economically viable structured knowledge acquisition with
high precision and recall
• …to increase the reach of what we can acquire
• Emphasizing understanding vs. applying black box models