Synonymy and Near-Synonymyin Deep Lexical Semantics

Niloofar Montazeri and Jerry R. Hobbs

Information Sciences Institute

University of Southern California

Marina del Rey, CA

Deep Lexical Semantics:Methodology

words

core theories

link with axioms

Construct core theories of abstract phenomena in various domains

Express meanings of word senses as logical axioms in terms of predicates in core theories

Core Theories and Lexical Periphery

Define (Characterize) words in terms supplied by the core theories.

range(x,y,z) <--> scale(s) &subscale(s1,s) & bottom(y,s1) & top(z,s1) & in(u1,x) & at(u1,y) & in(u2,x) & at(u2,z) & (u x)( v s1) at(u,v)

Axiomatize core theories with richly explicated core predicates:

Core Theory of Scales: scale, <, subscale, top, bottom, at

s s1y zv

x = {u1 . . . . . . . u . . . . . u2}

word: “range”

linkingaxiom

core theory

Abstract Words in Context

By specializing “at” and the scale in various ways, we can get a whole range of possible meanings for “range”:

The scores on the test ranged from 33 to 96. The timber wolf ranges from Mexico to the Arctic. His behavior ranged from cheerful to sullen.

Deep Lexical Semanticsof Event-Related Words

(forall (x y z) (iff (give x y z) (exist (e1 e2 e3) (and (cause x e1)(change’ e1 e2 e3)(have’ e2 x y) (have’ e3 z y)))))

x gives y to z = x causes a change from x having y to z having y

Builds on old work by Gruber, Lakoff, Schank, Jackendoff, and many others on lexical decomposition, but ....

“primative” predicates are explicated in core theories:

restricts possible interpretations of the predicates enables reasoning within theory logic, not syntax

Use in Textual Inference T: Russia is blocking oil from entering Ukraine.H: Oil cannot be delivered to Ukraine.

not’(n2,c2) & can’(c2,x2,d2) & deliver’(d2,x2,o2,u2)

block’(b1,x1,e1) & enter’(e1,o1,u1)

cause’(c1,x1,n1) & not’(n1,p1) & possible’(p1,e1)

cause’(d2,x2,c3) & changeTo’(c3,h2) & have’(h2,u2,o2)

in’(h2,o2,u2)possible’(p1,c4) & cause’(c4,x3,e1)

Defeasibleinferences

fromcore theories

Lexicaldecomposition

axioms

Core Theories: Change

change(e1,e2): state e1 changes into state e2

e1 and e2 involve a common entity; change of state of something

Transitive if the something is the same

e1 and e2 are different unless there is an intermediate state (cyclic change)

“move” is change of state of “at” relation

change(e1,e2) = changeFrom(e1) = changeTo(e2)

Theory of Causality: Causal Complex

e1 e2

e3 e4e

....

s

causal-complex(s,e)

e1 s, ....

When every event or state in s happens or holds, then e happens or holds.

All eventualities in s are relevant to the effect.

A rigorous, nondefeasible notion, but can’t specify everything.

causal complex

effect

Theory of Causality: Cause

In a causal complex, some eventualities are distinguished as causes.

power on

finger insocket

shock

What is presumable depends on task, context, knowledge base, ....“Cause” is a useful but defeasible notion.

presumable

cause

Causes are the focus ofplanning, prediction,

explanation, interpretingdiscourse

(but not diagnosis)

Methodology

Having axiomatized these core theories, ....

Focus on most common 450 word senses involving change of state and causality

Determine radial structure of set of WordNet senses of word, and characterize by incremental differences in associated axioms

Encode axioms for the most abstract or general senses or “supersenses”

Evaluate on a textual entailment task

“Enter”

enter-S1: x enters p = changeTo(p(x)) enter-V2: enter a race enter-V4: enter into calculations enter-V9: enter into career

enter-S11: x enters y = changeTo(in(x,y)) enter-V1: enter a room enter-V6: enter from stage left

enter-S2: x enters y in z = cause(x,enter-S11(y,z)) enter-V5: enter in ledger enter-V8: enter picture into text

p = at/in

+ cause

Logically, and sometimeschronologically

At each hop, we specialize a predicate orconstrain an argument

“Hit”

hit-S1: x hits y = changeTo(at(x,y)) We hit Detroit by noon. The temperature hit -20.

hit-S11: x hits y = changeTo’(e,in(x,y)) & sudden(e) & impact(x,y) The car hit a tree.

hit-S2: x hits z with y = cause(x,hit-S11(y,z)) He hit the ball.

+ sudden impact

+ cause

Supersenses give topological structure.Specific senses specialize general predicates or put constraints on arguments

Synonymy and Near-Synonymy

Synonymy: The axiomatic lexical decompositions are the same

Near-synonymy: The axiomatic lexical decompositions differ only incrementally (similar to word senses in radial structure)

“Receive” and “Get”

receive-S1: x receives y from z = change(have(z,y), have(x,y)) -> changeTo(have(x,y)) = FrameNet sense 1 subsumes all of WordNet’s senses where “have” is specialized to owning, having a property, perceiving, hosting, etc.

get-S11: x gets y = cause(x, changeTo(have(x,y))) He always gets what he wants. I’ll get the book at the library.

get-S12: x gets y = changeTo(have(x,y)) He got the flu. I got a call from Sue. You’ll get your results tomorrow.

Synonyms ornear-synonyms

“Go”, “Hit”, and “Reach”

go-V1: x goes from e1 to e2 = change(e1,e2) & arg*(x,e1) & arg*(x,e2)

go-FV3: specialize e1 and e2 to “at” relations x goes from y to z = change(at(x,y), at(x,z))

hit-S1: x hits z = changeTo(at(x,z))

reach-S1: x reaches z = changeTo(at(x,z))

x is an argument of ei or a participant in ei

3rdFrameNet

sense

Not synonymous atmore general levels

of “go”

Not synonymous atmore specific levels

of “hit”

“Deliver”, “Give”, and “Provide”

deliver-S1: x delivers y from w to z = cause(x, change(rel(y,w), rel(y,z)))

deliver-S11: specializes rel to havestipulate that x = w = cause(x, change(have(x,y), have(z,y)))

give-S0: cause(x, exist(y))

give-S1: cause(x, change(have(x,y), have(z,y)))

provide-S1: x provides z with y = cause(x, changeTo(possible(e))) & arg(y,e) & need(z,e) provide for medical emergencies

provide-S11: possible(e) specializes to have(z,y) = cause(x, changeTo(have(z,y))) & need(z,e)

synonyms

near synonyms

An Aside on “Need”

In a core theory of cognition (based on beliefs and goals),

Define badFor(e,x) = e causes a goal of x’s not to happen

need(x,e) = cause(not(e), e1) & badFor(e1,x)

(Gordon& Hobbs)

Overlapping Radial Structures

deliver-S1

deliver-S11

give-S0

give-S1

provide-S1

provide-S11

A Problem

Synonymy is a relation between word senses.

Carving the uses of words into word senses is highly arbitrary; e.g., WordNet is very fine-grained; VerbNet less so.

word-1, sense-iword-2, sense-j

Why not just stipulate that these are three different senses, so that in the middle the senses are perfectly synonymous?

Context-Dependent Synonymy

give = cause to have

provide = cause to have + need

She gave food to the hungry man.

She provided food for the hungry man.

The sentences are equivalent even though “give” and “provide” are only near synonyms.

Synonymous Specific Senses

So far the examples have been at an abstract level, but intersection of radial structures can occur at very specific levels too:

deliver-V1: deliver a talkgive-V12: give a talk

Small Perspective Differences:Near Synonyms?

hold: x holds e = cause(x, not(changeFrom(e))) hold that pose

Specialize e to at: cause(x, not(changeFrom(at(y,z)))) hold the picture against the wall

block: x blocks e = cause(x, not(changeTo(e))) The senator blocked the judge’s appointment

Specialize e to at: cause(x, not(changeTo(at(y,z)))) He blocked my way

These can describe the same situation: hold(x,e) = block(x,not(e))

“Capture” and “Seize”

x holds y = cause(x, not(changeFrom(at(y,z))))

x captures y = cause(x, changeTo(hold(x,y)))

“Seize”: Almost all senses of “seize” subsumed under

seize-S1: x seizes y = cause’(e, x, changeTo(hold(x,y))) & forceful(e)

nearsynonyms

Text Entailment Example:Synonymy from Inference

H: The captors let the hostage go free.

T: A Filipino hostage in Iraq was released.

release(x,y,z)

changeFrom’(e0,e1) & cause’(e1,x,e2) & not’(e2,e3) & changeFrom’(e3,e4) & at’(e4,y,z)

not(e5) & cause’(e5,x,e6) & not’(e6,e7)

changeTo’(e7,e8)

let(x,e7) & go’(e17,y,e8) & free’(e8,y,c,e9)

not’(e8,e10) & cause(e10,c,e11) & not’(e11,e9) & move’(e9,y,z,w)

changeFrom’(e9,e12) & at’(e12,y,z)Rexist(e0)

“Blunder” and “Lapse”

blunder(e,x) = error(e,x) & cause(e1,e) & stupid’(e1,x)

lapse(e,x) = error(e,x) & cause(e1,e) & neglectful’(e1,x)

Need to capture meaning of “error” in theory of composite entities as a mismatch between a composite entity and a pattern that serves as a norm.

Need to capture meanings of “stupid” and “neglect” in theory of cognition; “stupid” in terms of ability to learn and reason “neglect” in terms of model of attention

(Gordon & Hobbs)

(Edmunds & Hirst)

Near Synonyms?

raise(x,y,z,w) = cause(x, change(at(y,z), at(y,w))) & above(w,z)

rise(y,z,w) = change(at(y,z), at(y,w)) & above(w,z)

Differ only by cause(x, ...)

raise(x,y,z,w) = cause(x, change(at(y,z), at(y,w))) & above(w,z)

lower(x,y,z,w) = cause(x, change(at(y,z), at(y,w))) & above(z,w)

Differ only in relation of w and z

To be near synonyms the words have to describe the same situations.

Message

The meanings of word senses can be expressed as axioms in terms of predicates from core theories of the relevant phenomena.

The word senses of any word form a radial structure, where adjacent nodes differ by incremental changes in the axioms (specializations of predicates, constraints on arguments).

Word senses of different words can have identical axiomatic decompositions; this is synonymy.

Word senses of different words can have nearly identical axiomatic decompositions -- is this near synonymy?

Is near synonymy a natural kind, about which we can make reliable judgments?

More important than labeling word senses as near synonyms is capturing the distinctions formally.