From Syntax to Knowledge
Representation:
Parc’s Bridge System
Lauri Karttunen and Annie Zaenen
Boulder, CO
Aug 2, 2011
Wednesday, August 3, 2011
Toward NL Understanding
Local Textual Inference
A measure of understanding a text is the ability to make
inferences based on the information conveyed by it.
Veridicality reasoning
Did an event mentioned in the text actually occur?
Temporal reasoning
When did an event happen? How are events ordered in time?
Spatial reasoning
Where are entities located and along which paths do they
move?
Causality reasoning
Enablement, causation, prevention relations between events
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Wednesday, August 3, 2011
Overview
PARC’s Bridge system
LFG and XLE
Process pipeline
Abstract Knowledge Representation (AKR)
Conceptual, contextual and temporal structure
Instantiability
Entailment and Contradiction Detection (ECD)
Concept alignment, specificity calculation, entailment as subsumption
Demo!
Wednesday, August 3, 2011
LFG (Lexical Functional Grammar)
One of two “Bay-Area Unification-based Grammar
Formalisms” (frenemies)
LFG -- Joan Bresnan, Ronal M. Kaplan (≈ 1980)
HPSG - (Head-Driven Phrase Structure Grammar)
Ivan Sag, Carl Pollard (≈ 1984)
Rejection of transformations: no movement, use
constraints (unification) to enforce non-local
dependencies. (Recall Ivan’s talk on last week.)
Unlike anything coming from MIT, LFG and HPSG had
computational implementations and comprehensive
hand-written grammars for many languages from the
very beginning.
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Wednesday, August 3, 2011
The early computational implementations of LFG (and
HPSG) were seen as an aid for grammar writers.The
XLE (Xerox Linguistic Environment) system started
out as a “Grammar Writer’s Workbench” for LFG
linguists. Practical applications came later.
The current XLE is differs from the other systems we
have looked at in that it constructs a special
representation to assist reasoning. This is called AKR
(abstract knowledge representation). It is a “flat”
collection of statements, no complex nesting as you
have in syntactic trees, no lambdas.
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Wednesday, August 3, 2011
XLE parser
Broad coverage, domain independent,
ambiguity enabled dependency parser
Robustness: fragment parses
From Powerset: .3 seconds per sentence for
125 Million Wikipedia sentences
Maximum entropy learning to find weights to
order parses
Accuracy: 80-90% on PARC 700 gold standard
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Wednesday, August 3, 2011
System Overview
Wednesday, August 3, 2011
System Overview
string
“A girl hopped.”
Wednesday, August 3, 2011
System Overview
string
“A girl hopped.”
Wednesday, August 3, 2011
LFG
Parser
syntactic F-structure
System Overview
string
“A girl hopped.”
syntactic F-structure
LFG
Parser
ru l
e
t
i
r
w
re
es
AKR
(Abstract Knowledge
Representation)‫‏‬
Wednesday, August 3, 2011
AKR representation
A collection of statements.
Wednesday, August 3, 2011
AKR representation
concept term
A collection of statements.
Wednesday, August 3, 2011
AKR representation
concept term
A collection of statements.
Wednesday, August 3, 2011
WordNet synsets
AKR representation
concept term
thematic
role
A collection of statements.
Wednesday, August 3, 2011
WordNet synsets
AKR representation
concept term
WordNet synsets
thematic
role
instantiability facts
A collection of statements.
Wednesday, August 3, 2011
AKR representation
concept term
WordNet synsets
thematic
role
instantiability facts
event time
A collection of statements.
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Ambiguity
management
with
choices
seeing with a telescope
girl with a telescope
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Basic structure of AKR
Wednesday, August 3, 2011
Basic structure of AKR
Conceptual Structure
concept terms represent individuals and events, linked to WordNet synonym
sets by subconcept declarations.
concepts typically have roles associated with them.
Syntactic ambiguity is encoded in a space of alternative choices.
Wednesday, August 3, 2011
Basic structure of AKR
Conceptual Structure
concept terms represent individuals and events, linked to WordNet synonym
sets by subconcept declarations.
concepts typically have roles associated with them.
Syntactic ambiguity is encoded in a space of alternative choices.
Contextual Structure
t is the top-level context, some contexts are headed by an event term.
Clausal complements, negation and sentential modifiers also introduce
contexts.
Contexts can be related in various ways such as veridicality.
Instantiability declarations link concepts to contexts.
Wednesday, August 3, 2011
Basic structure of AKR
Conceptual Structure
concept terms represent individuals and events, linked to WordNet synonym
sets by subconcept declarations.
concepts typically have roles associated with them.
Syntactic ambiguity is encoded in a space of alternative choices.
Contextual Structure
t is the top-level context, some contexts are headed by an event term.
Clausal complements, negation and sentential modifiers also introduce
contexts.
Contexts can be related in various ways such as veridicality.
Instantiability declarations link concepts to contexts.
Temporal Structure
Locating events in time.
Temporal relations between events.
Wednesday, August 3, 2011
Temporal Structure
Wednesday, August 3, 2011
Temporal Structure
trole(when, talk:6,interval(before,Now))
Shared by “Ed talked.” and “Ed did not talk.”
Wednesday, August 3, 2011
Temporal Structure
trole(when, talk:6,interval(before,Now))
Shared by “Ed talked.” and “Ed did not talk.”
“Bill will say that Ed talked.”
Now
talk
say
say
trole(when,say:45,interval(after,Now))
trole(ev_when,talk:68,interval(before, say:45))
Wednesday, August 3, 2011
Conceptual Structure
q
Captures basic predicate-argument structures
q
Maps words to WordNet synsets
q
Assigns thematic roles
subconcept(talk:4, [talk-1,talk-2,speak-3,spill-5,spill_the_beans-1,lecture-1])
role(sb, talk:4, Ed:1)
subconcept(Ed:1, [male-2])
alias(Ed:1, [Ed])
role(cardinality_restriction,Ed:1,sg)
Shared by “Ed talked”, “Ed did not talk” and “Bill will say that Ed talked.”
Wednesday, August 3, 2011
Prime semantics vs. Wordnet semantics
What is the meaning of life?
Montague 1970:
life'
WordNet:
a cloud of synonym sets (14) in an ontology of
hypernyms
In prime semantics, lexical reasoning requires axioms (meaning
postulates).
In Wordnet semantics, some lexical reasoning can be done with
the synsets and hypernyms.
Wednesday, August 3, 2011
earth and ground intersect
earth
Sense 3
earth, ground
=> material, stuff
=> substance, matter
=> physical entity
=> entity
ground
Sense 3
land, dry land, earth, ground, solid ground, terra firma
=> object, physical object
=> physical entity
=> entity
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Equivalence
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level3 is a hypernym of plane3
level
1. degree, grade, level
=> property
2. grade, level, tier
=> rank
3. degree, level, stage, point => state
4. level
=> altitude, height => altitude
5. level, spirit level
=> indicator
6. horizontal surface, level => surface
7. floor, level, storey, story => structure
8. level, layer, stratum
=> place
plane
1. airplane, aeroplane, plane
=> heavier-than-air craft
2. plane, sheet
=> shape, form
3. plane
=> degree, level, stage, point
4. plane, planer, planing machine
=> power tool, => tool
5. plane, carpenter's plane, woodworking plane => edge tool, => hand tool
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One-way entailment
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Contextual Structure
q
q
q
t is the top-level context
the head of the context is typically an event concept
contexts can serve as roles such as object
Bill said that Ed wanted to talk.
context(t)
context(ctx(talk:29))
context(ctx(want:19))
top_context(t)
context_relation(t,ctx(want:19),crel(comp,say:6))
context_relation(ctx(want:19),ctx(talk:29),crel(ob,want:19))
The head of the context, want:19,
is used to name the context.
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ctx(want:19) is the
object of say:6 in t
Instantiability
Wednesday, August 3, 2011
Instantiability
An instantiability assertion of a concept-denoting term in a context
implies the existence of an instance of that concept in that context.
Wednesday, August 3, 2011
Instantiability
An instantiability assertion of a concept-denoting term in a context
implies the existence of an instance of that concept in that context.
Wednesday, August 3, 2011
Instantiability
An instantiability assertion of a concept-denoting term in a context
implies the existence of an instance of that concept in that context.
An uninstantiability assertion of a concept-denoting term in a context
implies there is no instance of that concept in that context.
Wednesday, August 3, 2011
Instantiability
An instantiability assertion of a concept-denoting term in a context
implies the existence of an instance of that concept in that context.
An uninstantiability assertion of a concept-denoting term in a context
implies there is no instance of that concept in that context.
If the denoted concept is of type event, then existence/nonexistence
corresponds to truth or falsity.
instantiable(girl:13, t) – girl:13 exists in t
Wednesday, August 3, 2011
Instantiability
An instantiability assertion of a concept-denoting term in a context
implies the existence of an instance of that concept in that context.
An uninstantiability assertion of a concept-denoting term in a context
implies there is no instance of that concept in that context.
If the denoted concept is of type event, then existence/nonexistence
corresponds to truth or falsity.
instantiable(girl:13, t) – girl:13 exists in t
instantiable(see:7, t) – see:7 is true in t
Wednesday, August 3, 2011
Instantiability
An instantiability assertion of a concept-denoting term in a context
implies the existence of an instance of that concept in that context.
An uninstantiability assertion of a concept-denoting term in a context
implies there is no instance of that concept in that context.
If the denoted concept is of type event, then existence/nonexistence
corresponds to truth or falsity.
instantiable(girl:13, t) – girl:13 exists in t
instantiable(see:7, t) – see:7 is true in t
uninstantiable(girl:13, t) – there is no girl:13 in t
Wednesday, August 3, 2011
Instantiability
An instantiability assertion of a concept-denoting term in a context
implies the existence of an instance of that concept in that context.
An uninstantiability assertion of a concept-denoting term in a context
implies there is no instance of that concept in that context.
If the denoted concept is of type event, then existence/nonexistence
corresponds to truth or falsity.
instantiable(girl:13, t) – girl:13 exists in t
instantiable(see:7, t) – see:7 is true in t
uninstantiable(girl:13, t) – there is no girl:13 in t
uninstantiable(see:7, t) – see:7 is false in t
Wednesday, August 3, 2011
Negation
“Ed did not talk”
Contextual structure
context(t)
context(ctx(talk:12)) new context triggered by negation
context_relation(t, ctx(talk:12), not:8)
antiveridical(t,ctx(talk:12))
interpretation of negation
Local and lifted instantiability assertions
instantiable(talk:12, ctx(talk:12))
uninstantiable (talk:12, t) entailment of negation
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Relations between contexts
Wednesday, August 3, 2011
Relations between contexts
Generalized entailment: veridical
If c2 is veridical with respect to c1,
the information in c2 is part of the information in c1
Lifting rule: instantiable(Sk, c2) => instantiable(Sk, c1)
Wednesday, August 3, 2011
Relations between contexts
Generalized entailment: veridical
If c2 is veridical with respect to c1,
the information in c2 is part of the information in c1
Lifting rule: instantiable(Sk, c2) => instantiable(Sk, c1)
Inconsistency: antiveridical
If c2 is antiveridical with respect to c1,
the information in c2 is incompatible with the info in c1
Lifting rule: instantiable(Sk, c2) => uninstantiable(Sk, c1)
Consistency: averidical
If c2 is averidical with respect to c1,
the info in c2 is compatible with the information in c1
No lifting rule between contexts
Wednesday, August 3, 2011
Determinants of context relations
Wednesday, August 3, 2011
Determinants of context relations
Relation depends on complex interaction of
Concepts
Lexical entailment class
Syntactic environment
Example
1. He didn’t remember to close the window.
2. He doesn’t remember that he closed the window.
3. He doesn’t remember whether he closed the window.
He closed the window.
Contradicted by 1
Implied by 2
Consistent with 3
Wednesday, August 3, 2011
Relative Polarity
Wednesday, August 3, 2011
Relative Polarity
Veridicality relations between contexts determined on
the basis of a recursive calculation of the relative
polarity of a given “embedded” context
Wednesday, August 3, 2011
Relative Polarity
Veridicality relations between contexts determined on
the basis of a recursive calculation of the relative
polarity of a given “embedded” context
Wednesday, August 3, 2011
Relative Polarity
Veridicality relations between contexts determined on
the basis of a recursive calculation of the relative
polarity of a given “embedded” context
Globality: The polarity of any context depends on the
sequence of potential polarity switches stretching
back to the top context
Wednesday, August 3, 2011
Relative Polarity
Veridicality relations between contexts determined on
the basis of a recursive calculation of the relative
polarity of a given “embedded” context
Globality: The polarity of any context depends on the
sequence of potential polarity switches stretching
back to the top context
Wednesday, August 3, 2011
Relative Polarity
Veridicality relations between contexts determined on
the basis of a recursive calculation of the relative
polarity of a given “embedded” context
Globality: The polarity of any context depends on the
sequence of potential polarity switches stretching
back to the top context
Top-down each complement-taking verb or other
clausal modifier, based on its parent context's
polarity, either switches, preserves or simply sets
the polarity for its embedded context.
Wednesday, August 3, 2011
Factives and Counterfactives
Class
Positive
Negative
Inference Pattern
forget that
forget that X ⊫ X, not forget that X ⊫ X
pretend that
pretend that X ⊫ not X, not pretend that X ⊫ not X
Abraham pretended that Sarah was his sister. ⇝ Sarah was not his sister
Howard did not pretend that it did not happen. ⇝ It happened.
Wednesday, August 3, 2011
Implicatives
Class
Two-way ++/-- manage to
implicatives +-/-+ fail to
Inference Pattern
manage to X ⊩ X, not manage to X ⊩ not X
fail to X ⊩ not X, not fail to X ⊩ X
++
force to
force X to Y ⊩ Y
+-
One-way
implicatives --
prevent from
prevent X from Ying ⊩ not Y
be able to
not be able to X ⊩ not X
-+
hesitate to
not hesitate to X ⊩ X
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Example: polarity propagation
Ed did not forget to force Dave to leave.
==> Dave left.
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not
comp
forget
subj
comp
Ed
force
subj
Ed
obj
Dave
comp
leave
subj
Dave
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not
+-/-+ Implicative
comp
forget
subj
comp
Ed
force
subj
Ed
obj
Dave
comp
leave
subj
Dave
Wednesday, August 3, 2011
not
+-/-+ Implicative
comp
forget
subj
+-/-+ Implicative
comp
Ed
force
subj
Ed
obj
Dave
comp
leave
subj
Dave
Wednesday, August 3, 2011
not
+-/-+ Implicative
comp
forget
subj
+-/-+ Implicative
comp
Ed
force
subj
Ed
obj
Dave
++ Implicative
comp
leave
subj
Dave
Wednesday, August 3, 2011
not
+-/-+ Implicative
comp
forget
subj
+-/-+ Implicative
comp
Ed
force
subj
Ed
obj
Dave
++ Implicative
comp
leave
subj
Dave
Wednesday, August 3, 2011
+
not
+-/-+ Implicative
comp
forget
subj
+-/-+ Implicative
comp
Ed
force
subj
Ed
obj
Dave
++ Implicative
comp
leave
subj
Dave
Wednesday, August 3, 2011
+
not
+-/-+ Implicative
-
comp
forget
subj
+-/-+ Implicative
comp
Ed
force
subj
Ed
obj
Dave
++ Implicative
comp
leave
subj
Dave
Wednesday, August 3, 2011
+
not
+-/-+ Implicative
-
comp
forget
subj
+-/-+ Implicative
Ed
force
subj
Ed
+
comp
obj
Dave
++ Implicative
comp
leave
subj
Dave
Wednesday, August 3, 2011
+
not
+-/-+ Implicative
-
comp
forget
subj
+-/-+ Implicative
Ed
force
subj
Ed
+
comp
obj
Dave
++ Implicative
+
comp
leave
subj
Dave
Wednesday, August 3, 2011
+
not
t
-
comp
forget
sb
Ed
force
sb
Ed
ob
Dave
+
comp
leave
subj
Dave
Wednesday, August 3, 2011
veridical
+
comp
ctx(leave:7)
+
not
t
-
comp
forget
sb
Ed
force
sb
Ed
ob
Dave
+
comp
leave
subj
Dave
Wednesday, August 3, 2011
veridical
+
comp
ctx(leave:7)
+
not
leave
-
comp
sb
forget
sb
Dave
force
sb
Ed
ob
Dave
+
comp
leave
subj
Dave
Wednesday, August 3, 2011
veridical
+
comp
Ed
t
ctx(leave:7)
Overview
PARC’s Bridge system
Process pipeline
LFG and XLE
Abstract Knowledge Representation (AKR)
Conceptual, contextual and temporal structure
Instantiability
Entailment and Contradiction Detection (ECD)
Concept alignment, specificity calculation, entailment as subsumption
Demo!
NatLog vs. Bridge
Wednesday, August 3, 2011
Kim hopped => Someone moved
Wednesday, August 3, 2011
More specific entails less specific
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How ECD works
Wednesday, August 3, 2011
How ECD works
Alignment
Wednesday, August 3, 2011
Context
t
Text:
Hypothesis:
t
Kim hopped.
Someone moved.
How ECD works
Alignment
Specificity
computation
Wednesday, August 3, 2011
Context
t
Text:
Hypothesis:
Text:
Hypothesis:
Kim hopped.
t
Someone moved.
t
Kim hopped.
t
Someone moved.
How ECD works
Alignment
Specificity
computation
Context
t
Text:
Hypothesis:
Text:
Hypothesis:
t
Someone moved.
t
Kim hopped.
t
Someone moved.
Text: t
Elimination of
H facts that are
Hypothesis: t
entailed by T facts.
Wednesday, August 3, 2011
Kim hopped.
Kim hopped.
Someone moved.
Alignment and specificity computation
Wednesday, August 3, 2011
Alignment and specificity computation
Context
Text:
t
Every boy saw a small cat.
Alignment
Wednesday, August 3, 2011
Hypothesis:
t
Every small boy saw a cat.
Alignment and specificity computation
Context
Text:
t
Every boy saw a small cat.
Alignment
Specificity
computation
Wednesday, August 3, 2011
Hypothesis:
t
Every small boy saw a cat.
Text:
t
Every boy saw a small cat.
Hypothesis:
t
Every small boy saw a cat.
Text:
t
Every boy saw a small cat.
Hypothesis:
t
Every small boy saw a cat.
Alignment and specificity computation
Context
Text:
t
Every boy saw a small cat.
Alignment
Specificity
computation
Every (↓) (↑)
Wednesday, August 3, 2011
Hypothesis:
t
Every small boy saw a cat.
Text:
t
Every boy saw a small cat.
Hypothesis:
t
Every small boy saw a cat.
Text:
t
Every boy saw a small cat.
Hypothesis:
t
Every small boy saw a cat.
Some (↑) (↑)
Contradiction:
instantiable --- uninstantiable
Wednesday, August 3, 2011
From English to AKR
Ed has been living in Athens for 3 years.
trole(duration,extended_now:13,interval_size(3,year:17))
trole(when,extended_now:13,interval(finalOverlap,Now))
trole(when,live:3,interval(includes,extended_now:13)
Mary visited Athens in the last 2 years.
trole(duration,extended_now:10,interval_size(2,year:11))
trole(when,extended_now:10,interval(finalOverlap,Now))
trole(when,visit:2,interval(included_in,extended_now:10))
Mary visited Athens while Ed lived in Athens.
trole(ev_when,live:22,interval(includes,visit:6))
trole(ev_when,visit:6,interval(included_in,live:22))
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Wednesday, August 3, 2011
From language to qualitative relations
of intervals and events
Ed has been living in Athens for 3 years.
Mary visited Athens in the last 2 years.
! Mary visited Athens
while Ed lived in Athens.
Construct interval
boundaries using
Aspect
Tense
Preposition meaning
Mary s visit to Athens
within
throughout
Left
boundary
Ed s living in Athens
NOW
Right
boundary
Inference from interval relations
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Wednesday, August 3, 2011
AKR modifications
Oswald killed Kennedy => Kennedy died.
P-AKR
aug
The situation improved.
=>
normalize
nt
e
m
AKR0
The situation became better.
sim
plify
Q-AKR
Kim managed to hop. => Kim hopped.
Wednesday, August 3, 2011
Overview
PARC’s Bridge system
LFG and XLE
Process pipeline
Abstract Knowledge Representation (AKR)
Conceptual, contextual and temporal structure
Instantiability
Entailment and Contradiction Detection (ECD)
Concept alignment, specificity calculation, entailment as subsumption
Demo!
NatLog vs. Bridge
Wednesday, August 3, 2011
Bridge vs. NatLog
NatLog
Symmetrical between t and h
Bottom up
Local edits, more compositional
Surface based
Integrates monotonicity and
implicatives tightly
Bridge
Asymmetrical between t and h
Top down
Global rewrites possible
Normalized input
Monotonicity calculus and
implicatives less tightly
We need more power than NatLog allows
but it needs to e deployed in a more constrained
way than the current Bridge system demonstrates.
40
Wednesday, August 3, 2011