PDF Situations are "Attractors" of Semantic Interpretations

Situations are

“Attractors” of Semantic Interpretations

Kow KURODA* Keiko NAKAMOTO**

Hajime NOZAWA* Hitoshi ISAHARA*

*National Institute of Information and Communications Technology (NICT), Japan

**Department of Education, Kyoto University, Japan

presented at

Corpus-based Approaches to Noncompositional Phenomena, A DGfS Workshop

Feb 23, 2006

Note: This PDF is a version with some modifications after the presentation

Overview

• Brief explanation for our previous work (Nakamoto, Kuroda and Nozawa 2005), an exhaustive corpus-

based detailed sematic analysis of Japanese verb osou inspired by the Frame Semantics/FrameNet approach (Fillmore 1985; Fillmore and Atkins 1994; Fillmore, et al. 2003)

• Define the Attraction-to-Situation Hypothesis

•

Propose a background theory of meaning construction

•

Define predictions to test experimentally based on the proposed theory

• ^Conclusion

2

Corpus Analysis

Background

• A detailed semantic analysis of Japanese verb osou (413 instances in total) was done exhaustively against a

corpus (500,000 Japanese-English alignments (JEA corpus; Utiyama and Isahara 2003)), adopting the Frame Semantics/FrameNet approach

• Main Results

•

Uses of osou are covered by 15 “situations,” or “semantic frames” at reasonably finer-grained granularities, including metaphoric and metonymic uses.

•

Figurative uses are bounded in that specific situations like

<Bank robbery>, <Invasion>, <Suffering a Disaster> that serve as “attractors” of semantic interpretations.

4

A previous corpus-

based analysis revealed the usage, both literal and figurative, of

Japanese verb osou (and its English counterparts) can be described for in terms of situation/frame hierarchy (in next slide) comprising of roughly 15 situations (F01-F15) at the lowest, most

specific levels.

English verbs that translate OSOU 112

(TOTAL) L0 = Sub L1 Level L1

Semantic Classes at Level 1

L2 Semantic Classes at

Level 2 L3 Semantic Classes

at Level 3

attack[+human(s)]: rob 4 7 10

Resource- threatenig situations

51 Intended Harm-

causation[+animate] 90 Cause oriented

attack[+human(s)]: rob: break into 2

attack[+human(s)]: rob: make off with MONEY 1

attack[+human(s)]: rob: hold up 1 3

attack[+human(s)]: rob: threaten 2

attack[+human(s)] 23 23 42

Life- threatening by

human

attack[+human(s)]: kill 1 1

attack[+human(s)]: assault 9 10

attack[+human(s)]: assault: raid 1

attack[+human(s)]: assault: shoot 3 5

attack[+human(s)]: assault: shoot, wound 1 attack[+human(s)]: assault: shoot; rob 1

attack[+human(s)]: assault: stab 3 3

attack[-human(s),+animal(s)] 7 8 9

Life- threatening by

nonhuman

attack[-human(s),+animal(s)]: kill 1

attack[-human(s),?animal]: assault[+metaphoric?]:

turn on 1 1

hit,strike: hit 3 8 18 Natural

disasters 39 Disasters = Harm- causation[-animate]

hit,strike: rock 1

hit,strike: strike 2

hit,strike: pound 2

hit,strike: destroy: wreak on 1 2

hit,strike: destroy: ravage 1

hit,strike: roar through 1 2

hit,strike: sweep through 1

hit,strike: wrought devastation 1 6

hit,strike: IMPLICIT in: earthquake 2

hit,strike: IMPLICIT in: in PLACE 2

hit,strike: there is 1

hit,strike[+metaphoric, +human(s)?]: occur[=attack] 1 2 21

Social disasters[+met

aphoric]

hit,strike[+metaphoric]: hurt 1

hit,strike[+metaphoric?]: hit 2 9

hit,strike[+metaphoric]: hit 5

hit,strike[+metaphoric]: paralyze 1

hit,strike[+metaphoric]: IMPLICIT in: shocks from 1

hit,strike[+metaphoric]: overtake 1 4

hit,strike[+metaphoric]: take a toll 1

hit,strike[+metaphoric]: besiege 1

hit,strike[+metaphoric]: engulf 1

hit,strike[+metaphoric]: occur 2 4

hit,strike[+metaphoric]: fall on 1

hit,strike[+metaphoric]: IMPLICIT in: in PLACE 1 hit,strike[+metaphoric]: IMPLICIT in: problems 1 2 hit,strike[+metaphoric]: IMPLICIT in: turmoil 1

suffer 3 5 10 Sufferings 10 Sufferings = Harm-

experience 10 Effect oriented

suffer: IMPLICIT in: victim 1

suffer: be injured 1

suffer: feel pain 1 3

Note: sampling in this table is partial:

112/413: JEA corpus has two

components: public and protected. 112 is the number of osou’s instances found in the public component. 413 is the number of osou’s occurrences in the

F07:

Nonpredatory Victimization

A,B,C,D,E (=ROOT):

Victimization of Y by X

A,B:

Victimization of Animal by

Animal

C,D,E:

Victimization by Disaster

F01,02,03:

Resource-aiming Victimization

F01,02: Power Conflict between

Human Groups

F03: Robbery

暴徒と化した民衆が警官隊を襲った A mob {attacked; ?assaulted} the squad of police.

貧しい国が石油の豊富な国を襲った

A poor country {attacked; ??assaulted} the oil-rich country.

F04: Persection

F05: Raping

三人組の男が銀行を襲った．

A gang of three {attacked; ??assaulted} the bank branch.

狂った男が小学生を襲った

A crazy man {attacked, assaulted} boys at elementary school.

男が二人の女性を襲った

A man {attacked; assaulted; ??hit} a young woman.

A: Victimization of Animal by

Animal

狼が羊の群れを襲った

Wolves {attacked; ?*assaulted} a flock of sheep.

スズメバチの群れが人を襲った A swarm of wasps {attacked; ?*assaulted} people.

F09,10(,11): Natural Disaster

D: Perceptible Impact

突風がその町を襲った

Gust of wind {?*attacked; hit; ?*seized} the town.

地震がその都市を襲った

An earthquake {*attacked; hit; ?*seized} the city.

ペストがその町を襲った

The Black Death {?*attacked; hit; ?seized} the town.

大型の不況がその国を襲った

A big depression {?*attacked; hit; ???seized} the country.

F12: Social Disaster

不安が彼を襲った He was seized with a sudden anxiety.

(cf. Anxiety attacked him suddenly}

肺癌が彼を襲った He {suffered; was hit by} a lung cancer (cf. Cancer {??attacked; hit; seized} him)

More Abstract More Concrete

暴走トラックが子供を襲った The children got victims of a runaway truck (cf. A runaway truck {*attacked; ?*hit} children.) F08: Accident

? C: Disaster

F01: Conflict between Human

Groups

?

F13,14,15: Getting Sick = Suffering a Mental or

Physical Disorder

F13: Long-term sickness

F14,15: Temporal Suffering a Mental or

Physical Disorder

F14: Short-term sickness

F15: Short-term mental disorder

無力感が彼を襲った He {suffered from; was seized by} inertia (cf. The inertia {?*attacked; ?hit; ?seized} him).

痙攣が患者を襲った The patient have a convulsive fit (cf. A convulsive fit {??attacked; ?seized him) F07a: Territorial

Conflict between Groups

F07b:

(Counter)Attack for Self-defense

サルの群れが別の群れを襲った

A group of apes {attacked; ?assaulted} another group.

MM 1d

MM* 2

MM 6a

F12a: Social Disaster on Larger Scale

F12b: Social Disaster on

Smaller Scale 赤字がその会社を襲った

The company {experienced; *suffered; went into} red figures.

(cf. Red figures {?attacked; ?hit; ?*seized} the company})

MM 4b MM 7a F09: Natural Disaster

on Smaller Scale

F10: Natural Disaster on Larger Scale MM 1b

MM 3b

MM 5b

NOTES

• Instantiation/inheritance relation is indicated by solid arrow.

• Typical “situations” at finer-grained levels are thick-lined.

• Dashed arrows indicate that instantiation relations are not guaranteed.

• attack is used to denote instantiations of A, B.

• assault is used to denote instantiations of B1.

• hit, strike are used to denote instantiations of C.

• Pink arrow with MM i indicates a metaphorical mapping:

Source situations are in orange.

MM 2

F11: Epidemic Spead B1: Victimization

of Human by Human

MM 1c

Hierarchical Frame Network (HFN) of “X-ga Y-wo osou” (active) and

“Y-ga X-ni osowareru” (passive)

E: Conflict between Groups

B1a: Physical Hurting =

Violence

F13,14: Suffering a Physical Disorder MM 1e

B: Victimization of Human by

Animal

MM 1a

MM 3a

MM 4a MM 5a

?

?MM 4c

?MM 7b

?MM 6b MM 0

E: Personal Disaster?

F02: Invasion F06: Predatory

Victimization

B1b: Abuse L2 Level Situations

L2 Level Situations L1 Level Situations

L1 Level Situations MM 8

?

マフィアの殺し屋が別の組織の組長を襲った A hitman of a Mafia {attacked; assaulted} the leader of the

opponents.

? B2: Victimization

of Human by Animal

6

Correspondence to Frames in Berkeley FrameNet

• Berkeley FrameNet provides three relevant frames:

•

<Attacking>

•

<Cause_impact>

•

<Cause_harm>

•

the first two of which correspond to pretty generic

situations of <(Intended) Harm-causation by Animate>, and <Unintended Harm-causation by Inanimate>, i.e., two semantic classes at the L2 level granularity. respectively

•

It is not clear what situation <Cause_harm> frame corresponds to.

Measuring Metaphoricality

• Loose Metaphoricality Index: Nonmetaphor =

“Threatenings by Human or Nonhuman”

•

0.455 = 1.0 - 62/ 112 [metaphors/all uses]

• Strict Metaphoricality Index 1: Nonmetaphor = “Life- or Resource-Threatenings by Human”

•

0.536 = 1.0 - 52/ 112 [metaphors/all uses]

• Strict Metaphoricality Index 2: Nonmetaphor = “Life- threatenings by Human or Nonhuman”

•

0.545 = 1.0 - 51/112 [metaphors/all uses]

8

Why Did We Do This?

• To estimate how much finer-granularity would be

needed if we decide on providing “realistic” semantic analysis/annotation as your goal

•

An on-going work on this will be presented at LREC 2006

• (Even) FrameNet frames turned out to be (too)

coarse-grained to give a realistic specification of the

“understood content” in unrestricted fashion.

•

“realistic” means specifying what (average) people

understand when they hear a sentence (wether in or out of context) as precise as is justifiable with experiments.

•

“unrestricted” means no assumed specific applications:

Information Retrieval, QA, Machine Translation

Metaphoric Uses “Dominate”

• Both “X-ga Y-wo osou” [active] and “Y-ga X-ni osowareru” [passive] were examined

•

While the two forms have different preferences for situations.

•

Virtually, more than half of the uses turned out to be metaphorical

• ^But

•

simply knowing how to detect metaphorical uses of a

word is not itself a “solution” to the most serious problem of specifying the “target” meaning of each meaning

transfer/metaphor.

10

What Drives Metaphorical Uses: Targets or Sources?

• Metaphorical uses would be more systematic than you expect, in that they look “bounded”.

• But they have a sparse distribution and therefore are less systematic than can be accounted for by

Conceptual Metaphor/Mapping Theory (Lakoff and Johnson 1980, 1999)

•

A set of conceptual metaphors like <Lust Is Hunger> just allow you to specify the necessary conditions for figurative uses; not the sufficient conditions: a lot of idiosyncratic — yet “understandable”— constraints are found for each metaphor

• But not enough time to elaborate on details today

Why Do Metaphoric Uses Look Bounded?

• Hard question to answer, but there is a hint:

•

What seems to determine the range of metaphoric uses is the “targets,” not “sources,” of metaphorical mappings/

meaning transfers.

• This led us to the idea/hypothesis that

•

the semantic interpretation of sentence s = w1 w2 ... wn is

“attracted” to a closed set of specific, understandable

situations specifiable in terms of semantic frames at finer- grained levels.

12

And when such attraction takes place

•

(lexical) meanings {

m

1,

m

2,

...

,

m

n} (

m

i =

m

(

w

i)) of the words, {

w

1,

w

2,

...

,

w

n}, are “adjusted” to the meaning of s.

•

This is a Gestalt effect in that (the meanings of) a “whole”

and its “parts” are given at the same time.

•

This is just a special case of “accommodation to a schema” in the Piagetian theory of conceptual development.

•

and it is an example of “semantic accommodation” in Cognitive Grammar framework (Langacker 1987, 1991, among others)

•

and it is also an example of “co-composition” in Generative Lexicon framework (Pustejovsky 1995)

•

But refinements are in need to get the basic ideas to work.

A “Competitive” Theory of Meaning Construction

— Defining a Framework —

A Theory to Test [1/2]

• Meaning construction is a competitive process like Darwinian natural selection, rather than (just) a (co)- compositional process, in that it has the “winner-take- all” property.

• A sentence “evokes” a set of “candidate”

interpretations, each with a “goodness of fit”

measured against “meaningfulness” models M* = {M

1

, M

2

, ..., M

n

}.

• Interpretation with the highest goodness of fit score

wins out (cf. Optimality Theory)

A Theory to Test [2/2]

• Interpretive models M is a set of conceivable

situations, or “semantic frames” at finer-granularities.

• This allows the following:

•

Each word in a sentence S evokes a set of unrelated situation/frames independently.

•

Yet meaning construction for S is successful as far as

situation-evocation “converges” after a “selection” process

•

(A lot of) semantics is “distributed” over combinations of words, rather than “symbolized” by single words.

•

as claimed in Collostructional Analysis (Stefanowitsch and Gries 2003; Gries and Stefanowitsch 2004)

16

How Competition Converges

•

Each “semantic unit” SU, not necessarily a word, within a sentence “activates” a set of (sub)frames independently.

•

Evoked (sub)frames compete each other either by

•

“activation”

•

or “(lateral) inhibition”

•

Once competition settles

down, the (meaning of) SUs of the “loser” (sub)frames

accommodate to the (meanings of) “winner”

(sub)frames

activates

activates

activates

activates activates

inhibits activates

inhibits inhibits

inhibits activates

Frame[1]

Frame Element[1]: ...

Frame Element[2]: ...

...Frame Element[n]: ...

Definition: ...

Frame[j]

Frame Element[1]: ...

Frame Element[2]: ...

...Frame Element[n]: ...

Definition: ...

Frame[k]

Frame Element[1]: ...

Frame Element[2]: ...

...Frame Element[n]: ...

Definition: ...

SU[n]

SU[i]

SU[1]

”Winner” (Sub)frames

”Loser“ (Sub)frame(s) activates

accomodates

activates

activates activates

inhibits activates

inhibits inhibits

inhibits activates

accomodates

Frame[1]

Frame Element[1]: ...

Frame Element[2]: ...

...Frame Element[n]: ...

Definition: ...

Frame[i]

Frame Element[1]: ...

Frame Element[2]: ...

...Frame Element[n]: ...

Definition: ...

Frame[k]

Frame Element[1]: ...

Frame Element[2]: ...

...Frame Element[n]: ...

Definition: ...

SU[n]

SU[i]

SU[1]

Remarks

• Some crucial aspects of the proposed model of meaning construction is not new at all.

•

The basic ideas come from Parallel Distributed Processing (PDP) model of cognitive processes (Rumelhart, et al.

1985; McClelland, et al. 1986)

•

A PDP-style situation/frame selection was already

implemented in G. Cottrell’s handcrafted, hardwired neural network for Word Sense Disambiguation (Cottrell 1985)

• But the proposed model still has something new to it.

•

Word senses are “modified” or even “generated” through their accommodation to the meaning of a sentence that they occur within.

18

Attraction-to-Situation Hypothesis

• As a result of “selection for goodness of fit,” semantic interpretations of a given sentence S are “attracted” to (ideally) one of the possible and very likely situations.

• This predicts the following:

•

Attraction-to-Situation Hypothesis: Attraction to situation is effective even if all arguments are not explicitly given.

• This can be tested experimentally using “semantic

feature rating” (SFR) method (Nakamoto, Kuroda and Nozawa 2005)

•

SFR: a word within a sentence is rated against a set of semantic/characteristic features

Prediction to Test

• If the theory is correct, a nonce word w* is feature- rated very much like a real word, if its occurring

context C(w*) = W(s) – w* evokes a situation strong enough.

• For English examples,

•

^C(

^w*

for Victim) = ___ {was attacked; was hit; suffered (from)} <Harm-causer>

•

^C(

^w*

for Harm causer) = <Victim> {was attacked; was hit;

suffered (from)} ___

• Is this a right prediction? — Let’s test it!

20

Testing Procedure

• Japanese sentence X-ga Y -wo osou [active], or Y -ga X-ni osowareru [passive]) ALWAYS denotes a situation in which Victimization of Y by a Harm-causer X occurs.

• Prediction confirms if SFRs for nonce words in C

1

, C

2

conditions are similar to real words in C

0

•

^C0: {X: Real, Y: Real, osou} neutral

•

^C1: {X: Real, Y: Nonce, osou} attraction by Harm-causer

•

^C2: {X: Nonce, Y: Real, osou} attraction by Victim

Situation ID Subject NP denoting Victim

PP denoting Harm- causer

Transliteration (word-by-word

translation from Japanese) Translation Original Example (in Passive form) F01: Power Conflict

between Human Groups The President an assasin The President was attacked by an assasin.

The President was assaulted by an

assasin. !"#$% &'( )*+,-

F02: Invasion a country an(other) armed

country

A country was attacked by another armed country.

A country was attacked by another

armed country. ./0% 12345( )*+,-

F03: Robbery on larger

scales a bank branch a masked man A bank branch was attacked by a masked man.

A bank branch was attacked by a

masked man. 678% 9:;<=>( )*+,-

F03: Robbery on smaller

scales an old lady a purse snatcher An old lady was attacked by a purse snatcher.

An old lady was assaulted by a purse

snatcher. !"?8% @A( )*+,-

F04: Persecution, Violence passengers-by a lunatic man Passengers-by were attacked by a lunatic man.

Passengers-by were assaulted by a

lunatic man. !"BC% DEFGH( )*+,-

F05: Rape, Sexually assault a woman a pervert A woman was attacked by a pervert. A woman was sexually assaulted a

pervert. !"I% JKI( )*+,-

F06: Preying animal attack;

Predation zebras lions Zebras were attacked by lions. Zebras were attacked by lions. !"LM% NOPQRS( )*+,-

F07: Nonpreying animal

attack, usually for defence children wasps Children were attacked by wasps. Children were attacked by wasps. !"TU% VWXYZW[=\7( )

*+,- F08: Accident a family a unrunaway truck A family was attacked by a runaway

truck.

A family got the victim of an accident by

a runaway truck. ]^U_% ]`=Na( )*+,-

F09: Natural disater on

smaller scales a town gust of wind A town was attacked by gust of wind. A town was hit by gust of wind. bcdc% QVeW( )*+,- F10: Natural disaster on

larger scales an area a hurricane An area was attacked by a hurricane. An area was hit by a hurricane. f7% gh=>( )*+,- F11: Epidemic spread a city influenza A city was attacked by influenza. A city was hit by influenza. !"?i% jk,lm( )*+,- F12: Social disaster the stock market a debacle The stock market was attacked by a

sharp fall. The stock market was hit by a sharp fall. !">n% Do( )*+,- F13: Long-term sickness a man cancer A man was attacked by cancer. A man was seized by cancer; A man

suffered cancer. !"pa% q'( )*+,-

F14,15: Short-term mental

disorder OR sickness an old man panic An old man was attacked by panic. An old man was seized by panic. !"rn% nstuC( )*+,- F14: Short-term sickness a man a sharp pain A man was attacked by a sharp pain. A man was seized by a sharp pain; A man

suffered a sharp pain. vwx% yzC( )*+,-

F15: Short-term mental

disorder a young man strong jealousy A young man was attacked by a strong jealousy

A young man was seized by a strong

jealousy !"8% {n=|W( )*+,-

NONE zebras a masked man Zebras were attacked by a masked man. Zebras were attacked by a masked man. bcdc% gh=>( )*+,-

Experiments

24 features used for SFR

Class ID English translation of rated feature Rated feature in Japanese

Harm-causer 1 X is a living thing. X!"#$%&'(

Harm-causer 2 X chose Y for its target. X!Y)*+%!,-

Harm-causer 3 X is visible. X!".#/'01%&'2

Harm-causer 4 X couldn't help doing it to Y. X3Y)!,-4!564789:;,-(

Harm-causer 5 X is human. X!<=%&'(

Harm-causer 6 X had an aim to do so. X!">)?,@!,-(

Harm-causer 7 X is a natural phenomenon. X!A$BC%&'(

Harm-causer 8 X did so to satisfy its desire or needs. X!AD4EF)G-H-I.!,-(

Harm-causer 9 X planned to take off something from Y. X!YJKLJ)MNOPQ;,-(

Harm-causer 10 X is the name for a sickness. X!RS%&'(

Harm-causer 11 X's activity can kill Y. X!Y)!,@T7U'9:3&'(

Harm-causer 12 X is a collection of living things. X!"#$4VWQ%&'(

Victim 1 Y is a living thing. Y!"#$%&'(

Victim 2 Y had a good chance to prepare for X's activity. Y3X4!X.Y/'4!Z[;,-(

Victim 3 Y had some reason to be victimized by X. Y.!X.!\]'LKJ4^_3&,-(

Victim 4 Y is human. Y!<=%&'(

Victim 5 Y was aware of being victimized by X. Y!X.!\]'`ab.cd8@8-(

Victim 6 X's activity on X may cause X to die. Y!X.!\]-43ef%Tg9:3&'(

Victim 7 Y is the name for a place. Y!hi)jHSk%&'(

Victim 8 Y could avoid X's harm. Y!X.!\]'4)lmH'9:P%#-(

Victim 9 The degree of Y's affectedness is greater than the

individual scale. Y4!\]6!n</n14%o)p/'(

Victim 10 Y suffered a harm by X's activity on Y. q!X.!\]@0r.st)uv-(

Victim 11 Y has been targeted by X long before. q!X.wkJKx\]@8-(

Full sentences (Baseline)

24

Situation ID Subject NP

denoting Victim

PP denoting Harm- causer

Transliteration (word-by-word

translation from Japanese assuming that osou translates to attack)

More Natural Translation Original Example (in Passive form) F01: Power Conflict

between Human Groups The President an assassin The President was attacked by an assassin.

The President was assaulted by an

assasin. !"#$%&'()*+,-

F02: Invasion a country an(other) armed

country

A country was attacked by another armed country.

A country was attacked by another

armed country. ./0$ 120( )*+,-

F03: Robbery on larger

scales a bank branch a masked man A bank branch was attacked by a masked man.

A bank branch was attacked by a masked

man. 34$ 5678( )*+,-

F03: Robbery on smaller

scales an old lady a purse snatcher An old lady was attacked by a purse snatcher.

An old lady was assaulted by a purse

snatcher. ./9:$ ;<,=>( )*+,-

F04: Persecution, Violence passengers-by a lunatic man Passengers-by were attacked by a lunatic man.

Passengers-by were assaulted by a lunatic

man. ?4@$ ABCD78( )*+,- F05: Rape, Sexual assault a woman a pervert A woman was attacked by a pervert. A woman was sexually assaulted a

pervert. ./EF$ FGHI'( )*+,-

F06: Preying animal attack;

Predation zebras lions Zebras were attacked by lions. Zebras were attacked by lions. JKLK$ MNOP( )*+,-

F07: Nonpreying animal

attack, usually for defence children wasps Children were attacked by wasps. Children were attacked by wasps. QRS$ TUVWX( )*+,- F08: Accident a family a unrunaway truck A family was attacked by a runaway

truck.

A family got the victim of an accident by

a runaway truck. ./YZ$ [\]M^_( )*+,-

F09: Natural disater on

smaller scales a town gust of wind A town was attacked by gust of wind. A town was hit by gust of wind. ./`a$ bc( )*+,- F10: Natural disaster on

larger scales an area a hurricane An area was attacked by a hurricane. An area was hit by a hurricane. ./de$fc()*+,-

F11: Epidemic spread a city influenza A city was attacked by influenza. A city was hit by influenza. ./gh$NPijkPl7m4()

*+,- F12: Social disaster the stock market a debacle (or a

downturn, sharp fall)

The stock market was attacked by a

sharp fall. The stock market was hit by a sharp fall. nohp$ nq7[a( )*+,-

F13: Long-term sickness a man cancer A man was attacked by cancer. A man was seized by cancer; A man

suffered cancer. ./@$rF7sP()*+,-

F14,15: Short-term mental

disorder OR sickness an old man panic An old man was attacked by panic. An old man was seized by panic. ./9@$ tu( )*+,- F14: Short-term sickness a man a sharp pain A man was attacked by a sharp pain. A man was seized by a sharp pain; A man

suffered a sharp pain. ./8F$ vw( )*+,-

F15: Short-term mental

disorder a young man strong jealousy A young man was attacked by a strong jealousy

A young man was seized by a strong

jealousy ./x'$vyz{|()*+,-

NONSENSICAL zebras a masked man Zebras were attacked by a masked man. Zebras were attacked by a masked man. JKLK$ 5678( )*+,-

Test sentences with a nonce word for Victim

Situation ID Subject NP

denoting Victim

PP denoting Harm- causer

Transliteration (word-by-word translation

from Japanese) Translation

F01: Power Conflict between

Human Groups Nonce Word an assasin ____ was attacked by an assasin. The President was assaulted by ___.

F02: Invasion Nonce Word an(other) armed

country

____ was attacked by another armed

country. A country was attacked by ___.

F03: Robbery on larger scales Nonce Word a purse snatcher ____ was attacked by a purse snatcher. An old lady was assaulted by ___.

F03: Robbery on smaller scales Nonce Word a masked man ____ was attacked by a masked man. A bank branch was attacked by ___.

F04: Persecution, Violence Nonce Word a lunatic man ____ were attacked by a lunatic man. Passengers-by were assaulted by ___.

F05: Rape; Sexually assault Nonce Word a pervert ____ was attacked by a pervert. A woman was sexually assaulted by ___.

F06: Preying animal attack;

Predation Nonce Word lions ____ were attacked by lions. Zebras were attacked by ___.

F07: Nonpreying animal attack,

usually for defence Nonce Word wasps ____ were attacked by wasps. Children were attacked by ___.

F08: Accident Nonce Word a unrunaway truck ____ was attacked by a runaway truck. A family got the victim of an accident by ___.

F09: Natural disater on smaller

scales Nonce Word gust of wind ____ was attacked by gust of wind. A town was hit by ___.

F10: Natural disaster on larger

scales Nonce Word a hurricane ____ was attacked by a hurricane. An area was hit by ___.

F11: Epidemic spread Nonce Word influenza ____ was attacked by influenza. A city was hit by ___.

F12: Social disaster Nonce Word a debacle ____ was attacked by a sharp fall. The stock market was hit by ___.

F13: Long-term sickness Nonce Word cancer ____ was attacked by cancer. A man was seized by ___; A man suffered (from) ___.

F14,15: Short-term mental

disorder OR sickness Nonce Word a sharp pain ____ was attacked by a sharp pain. A man was seized by ___; A man suffered (from) ___.

F14: Short-term sickness Nonce Word panic ____ was attacked by panic. An old man was seized by ___.

F15: Short-term mental

disorder Nonce Word strong jealousy ____ was attacked by a strong jealousy A young man was seized by ___.

Test sentences with a nonce word for Harm-causer

Situation ID Subject NP

denoting Victim

PP denoting Harm- causer

Transliteration (word-by-word translation

from Japanese) Translation

F01: Power Conflict between

Human Groups The President Nonce Word The President was attacked by ___. ____ was assaulted by an assasin.

F02: Invasion a country Nonce Word A country was attacked by ___. ____ was attacked by another armed country.

F03: Robbery on larger scales an old lady Nonce Word An old lady was attacked by ___. ____ was assaulted by a purse snatcher.

F03: Robbery on smaller

scales a bank branch Nonce Word A bank branch was attacked by ___. ____ was attacked by a masked man.

F04: Persecution, Violence passengers-by Nonce Word Passengers-by were attacked by ___. ____ were assaulted by a lunatic man.

F05: Rape, Sexually assault a woman Nonce Word A woman was attacked by ___. ____ was sexually assaulted by a pervert.

F06: Preying animal attack;

Predation zebras Nonce Word Zebras were attacked by ___. ____ were attacked by lions.

F07: Nonpreying animal attack,

usually for defence children Nonce Word Children were attacked by ___. ____ were attacked by wasps.

F08: Accident a family Nonce Word A family was attacked by ___. ____ got victimized of an accident by a runaway truck.

F09: Natural disater on

smaller scales a town Nonce Word A town was attacked by ___. ____ was hit by gust of wind.

F10: Natural disaster on larger

scales an area Nonce Word An area was attacked by ___. ____ was hit by a hurricane.

F11: Epidemic spread a city Nonce Word A city was attacked by ___. ____ was hit by influenza.

F12: Social disaster the stock market Nonce Word The stock market was attacked by ___. ____ was hit by a debacle.

F13: Long-term sickness a man Nonce Word A man was attacked by ___. ____ was seized by cancer; ___ suffered (from) cancer.

F14,15: Short-term mental

disorder OR sickness a man Nonce Word A man was attacked by ___. ____ was seized by a sharp pain; ___

suffered a sharp pain.

F14: Short-term sickness an old man Nonce Word An old man was attacked by ___. ____ man was seized by panic.

F15: Short-term mental

disorder a young man Nonce Word A young man was attacked by ___. ____ man was seized by a strong jealousy.

NONE Nonce Nonce Word ___ were attacked by ___. ____ were attacked by ___.

26

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Results

Method for Analysis

• Principal Component Analysis (PCA) is done to behavioral data collected from the SFR task

•

all data are averaged

•

data showing over-variance are managed (over-variance suggests multiple attractors)

• It was to see

•

if there are any determinants, i.e. Principal Components (PCs), that account for the patterning in positive evidence.

•

to represent, in terms of locations in a reduced, multi-

dimensional space, how relevant situations are interrelated to each other, giving you some measures of the distances among them.

PCA Plot:

real and nonce words for Victim, (condition C1), measuring

strengths of situation

evocation by Harm-causer- denoting nouns

President – assassin

country – armed country

bank branch – masked man

woman – pervert

passengers-by – lunatic man zebras -

lions

children - wasps

family – runaway truck

area - hurricane

town – gust of wind

city - influenza

stock market - debacle

man - cancer man –

sharp pain

young man – strong jealousy old man - panic

Text

+ at the center is the

“neural” point, where NW- ga NW-ni osowareta (“NW {was attacked by; was hit by;

suffered (from)} NW”) is located.

PCA Plot:

real and nonce words for Victim, (condition C1), measuring

strengths of situation

evocation by Harm-causer- denoting nouns

President – assassin

country – armed country

bank branch – masked man

woman – pervert

passengers-by – lunatic man zebras -

lions

children - wasps

family – runaway truck

area - hurricane

town – gust of wind

city - influenza

stock market - debacle

man - cancer man –

sharp pain

young man – strong jealousy old man - panic President –

assassin

country – armed country

bank branch – masked man

woman – pervert

passengers-by – lunatic man zebras -

lions

children - wasps

family – runaway truck

area - hurricane

town – gust of wind

city - influenza

stock market - debacle

man - cancer man –

sharp pain

young man – strong jealousy old man - panic

30

Text

Arrows indicate

correspondences between nonce word ratings and real word ratings

Thickness of arrows encodes strength

PCA Plot:

real and nonce words for Harm- causer (condition C2), measuring strengths of situation

evocation by

Victim-denoting nouns

Compared to C1,

attraction is weak, and long

President –assassin country –

armed country

bank branch – masked man old lady – purse snatcher woman –

pervert passengers-by –

lunatic man

zebras - lions children -

wasps

family – runaway truck area –

hurricane town – gust of wind

city - influenza

stock market – debacle

man - cancer

man – sharp pain

old man - panic

young man – strong jealousy

PCA Plot:

real and nonce words for Harm- causer (condition C2), measuring strengths of situation

evocation by

Victim-denoting nouns

Compared to C1,

attraction is weak, and long

President –assassin country –

armed country

bank branch – masked man old lady – purse snatcher woman –

pervert passengers-by –

lunatic man

zebras - lions children -

wasps

family – runaway truck area –

hurricane town – gust of wind

city - influenza

stock market – debacle

man - cancer

man – sharp pain

old man - panic

young man – strong jealousy

President –assassin country

–armed country

bank branch – masked man old lady – purse snatcher woman –

pervert passengers-by –

lunatic man

zebras - lions children -

wasps

family – runaway truck area –

hurricane town – gust of wind

city - influenza

stock market –debacle

man - cancer

man – sharp pain

old man - panic

young man – strong jealousy

32

Results

• Certain nonce words w* are feature-rated very much like real words, showing that their occurring contexts C(w*) = W(s) – w* evoke situations strong enough.

• This confirms our prediction, and A-to-S Hypothesis is not falsified.

• ^But

•

Clearly, different nouns have different strengths of situation-evocation

•

Some nouns (e.g., ansatu-sha ‘assassin’) showed stronger evocation effect; others (e.g., fukumen-no otoko ‘a masked man’) don’t show so much effect

Conclusion

A-to-S Hypothesis Confirmed; Yet ...

• Overall, stronger A-to-S effect was found for Harm- causer nouns than for Victim nouns

•

because nouns for Harm-causer evoke situations stronger than nouns for Victim when used with osou?

• Different nouns have different strengths of situation evocation

•

This supports the hypothetical distinction between role names from object names (Kuroda and Isahara 2005)

•

cf. Gentner (2005)’s relational nouns and object/entity nouns distinction

Why Distinguish Role Names from Object Names?

• Nouns like victim, robbery, prey, predator, disaster, are role-denoting nouns distinguished from object-

denoting nouns like (a) man, (a) typhoon

• Instantiation relation (i.e., IS-A relation) is definable between object-denoting and role-denoting nouns:

•

[typhoon] IS-A [disaster], [flood] IS-A [disaster], etc.

•

[three people (wounded in the accident)] IS-A [victim]

• This is a piece of information missing in most thesauri.

• Not surprisingly, role names play more important a role in metaphorical mappings than object names (Nakamoto, Kuroda, and Kusumi, under review)

36

General Remarks

• Certain nouns, if not all, evoke situations or frames.

•

probably independently from verbs and prepositions

•

or collaboratively with verbs and prepositions?

• Situation evocation by nouns (direct evocation by role-denoting nouns; indirect evocation by object-

denoting nouns), needs to be taken care of if we want to deal with noncompositional phenomena, including metaphor, successfully.

•

And a detailed specification and the successful description of it will supplement co-compositional processes given qualia structures (Pustejovsky 1995).

Acknowledgments

• Toshiyuki Kanamaru, Kyoto University, NICT

• Jae-ho Lee, NICT

• Masao Utiyama, NICT

38

Thanks for Your Attention

and Your Tolerance for My Not-So-

Good English

After-thanks Slides

PCA Factor Loadings

Feature ID Feature translated in English PC1 PC2 PC3 if any

Victim05 Y was aware of the possibility of victimization by X. -0.648 0.333 0.046

Victim02 Y had a good chance to prepare for X's activity. -0.641 0.199 0.183

Victim09 The degree of Y's affectedness is greater than the individual scale. -0.555 -0.398 -0.166

Victim08 Y could avoid X's affect on it. -0.537 0.397 0.116

Victim11 Y has been targeted by X long before. -0.535 0.303 -0.196

Victim01 Y is a living thing. 0.484 0.725 -0.153

Victim04 Y is human. 0.386 0.676 -0.088

Victim03 Y had some reason to be victimized by X. -0.366 0.551 0.278

Victim12 Y itself might have invited X's activity on it. -0.442 0.550 0.207

Victim07 Y is the name for a place. -0.549 -0.664 -0.018

Victim06 X's activity on X may cause X to die. -0.134 0.313 -0.746

Victim10 Y suffered a harm by X's affect on it. -0.286 -0.029 -0.713

Variance explained 2.826 2.686 1.333

% of variance 0.236 0.224 0.111

cummulative % 0.236 0.459 0.570

Interpretations

PC1: Unpreditability of harm from X PC2: Number of sufferers (Scale)

Harm-causer03 X is visible. 0.829 0.089

Harm-causer02 X chose Y for its target. 0.828 0.029

Harm-causer08 X did so to satisfy its desire or needs. 0.813 -0.055

Harm-causer01 X is a living thing. 0.805 0.229

Harm-causer05 X is human. 0.779 -0.163

Harm-causer09 X planned to take off something from Y. 0.721 -0.037

Harm-causer07 X is a natural phenomenon. -0.693 0.313

Harm-causer11 X's activity can kill Y. 0.238 0.669

Harm-causer12 X is a collection of living things. 0.286 0.659

Harm-causer10 X is the name for a sickness. -0.388 0.485

Harm-causer04 X couldn't help doing it to Y. -0.134 0.196

Variance explained 4.596 1.345

% of variance 0.418 0.122

cummulative % 0.418 0.540

Interpretations