slides used for the presentation at DGFS 2006

1. 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

2. 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

3. Corpus Analysis

4. 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

5. A previous corpus-
Semantic
112 L0 = Sub Semantic Classes at Semantic Classes
English verbs that translate OSOU L1 Classes at L2 L3
(TOTAL) L1 Level Level 2 at Level 3
Level 1
Resource-
based analysis revealed
Intended Harm-
attack[+human(s)]: rob 4 7 10 threatenig 51 90 Cause oriented
causation[+animate]
situations
attack[+human(s)]: rob: break into 2
attack[+human(s)]: rob: make off with MONEY 1
the usage, both literal attack[+human(s)]: rob: hold up
attack[+human(s)]: rob: threaten
1
2
3
Life-
and figurative, of
attack[+human(s)] 23 23 42 threatening by
human
attack[+human(s)]: kill 1 1
attack[+human(s)]: assault 9 10
Japanese verb osou (and attack[+human(s)]: assault: raid
attack[+human(s)]: assault: shoot
attack[+human(s)]: assault: shoot, wound
1
3
1
5
its English counterparts)
attack[+human(s)]: assault: shoot; rob 1
attack[+human(s)]: assault: stab 3 3
Life-
attack[-human(s),+animal(s)] 7 8 9 threatening by
can be described for in attack[-human(s),+animal(s)]: kill
attack[-human(s),?animal]: assault[+metaphoric?]:
1
1 1
nonhuman
terms of situation/frame
turn on
Natural Disasters = Harm-
hit,strike: hit 3 8 18 39
disasters causation[-animate]
hit,strike: rock 1
hierarchy (in next slide)
hit,strike: strike 2
hit,strike: pound 2
hit,strike: destroy: wreak on 1 2
hit,strike: destroy: ravage 1
comprising of roughly hit,strike: roar through
hit,strike: sweep through
hit,strike: wrought devastation
1
1
1
2
6
15 situations (F01-F15)
hit,strike: IMPLICIT in: earthquake 2
hit,strike: IMPLICIT in: in PLACE 2
hit,strike: there is 1
Social
at the lowest, most hit,strike[+metaphoric, +human(s)?]: occur[=attack]
hit,strike[+metaphoric]: hurt
1
1
2 21 disasters[+met
aphoric]
specific levels.
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
Note: sampling in this table is partial: hit,strike[+metaphoric]: take a toll
hit,strike[+metaphoric]: besiege
1
1
hit,strike[+metaphoric]: engulf 1
112/413: JEA corpus has two hit,strike[+metaphoric]: occur 2 4
hit,strike[+metaphoric]: fall on 1
components: public and protected. 112 hit,strike[+metaphoric]: IMPLICIT in: in PLACE
hit,strike[+metaphoric]: IMPLICIT in: problems
1
1 2
is the number of osou’s instances found hit,strike[+metaphoric]: IMPLICIT in: turmoil 1
Sufferings = Harm-
suffer 3 5 10 Sufferings 10 10 Effect oriented
in the public component. 413 is the suffer: IMPLICIT in: victim 1
experience
number of osou’s occurrences in the suffer: be injured
suffer: feel pain
1
1 3
entire JEA corpus.
suffer: bring sorrow to people 1
suffer: feel anxiety 1
suffer: seized with 1 4
suffer: suddenly begin a SYMPTOM 1
5
suffer: experience attack[-human(s), +metaphoric] 2

6. More Abstract More Concrete
狼が羊の群れを襲った
L2 Level Situations L1 Level Situations Wolves {attacked; ?*assaulted} a flock of sheep.
F06: Predatory
Victimization MM 1a
F07b:
(Counter)Attack for スズメバチの群れが人を襲った
Self-defense A swarm of wasps {attacked; ?*assaulted} people.
A: Victimization MM 1c
of Animal by
MM 1b
Animal
F07:
サルの群れが別の群れを襲った
Nonpredatory A group of apes {attacked; ?assaulted} another group.
Victimization F07a: Territorial
B2: Victimization Conflict between
A,B:
of Human by MM 1d Groups
F01: Conflict
マフィアの殺し屋が別の組織の組長を襲った
Animal
Victimization of between Human A hitman of a Mafia {attacked; assaulted} the leader of the
Animal by MM* 2 Groups
Animal E: Conflict
MM 2
opponents.
between
Groups
暴徒と化した民衆が警官隊を襲った
B: Victimization F01,02: Power
of Human by Conflict between A mob {attacked; ?assaulted} the squad of police.
Animal Human Groups
MM 1e
F01,02,03: 貧しい国が石油の豊富な国を襲った
Resource-aiming F02: Invasion
A poor country {attacked; ??assaulted} the oil-rich country.
Victimization
B1: Victimization B1a: Physical
of Human by Hurting =
F03: Robbery 三人組の男が銀行を襲った．
Human Violence
A gang of three {attacked; ??assaulted} the bank branch.
F04: Persection
狂った男が小学生を襲った
B1b: Abuse
A crazy man {attacked, assaulted} boys at elementary school.
F05: Raping
A,B,C,D,E (=ROOT): 男が二人の女性を襲った
Victimization of Y
by X A man {attacked; assaulted; ??hit} a young woman.
突風がその町を襲った
F09: Natural Disaster
?MM 7b on Smaller Scale Gust of wind {?*attacked; hit; ?*seized} the town.
F09,10(,11): Natural
Disaster MM 7a
MM 0
?MM 6b F10: Natural Disaster 地震がその都市を襲った
on Larger Scale
? An earthquake {*attacked; hit; ?*seized} the city.
D: Perceptible
Impact MM 6a
F11: Epidemic
Spead MM 3a ペストがその町を襲った
The Black Death {?*attacked; hit; ?seized} the town.
MM 3b F12a: Social Disaster on
? Larger Scale
C,D,E: 大型の不況がその国を襲った
Victimization
by Disaster C: Disaster A big depression {?*attacked; hit; ???seized} the country.
F12: Social Disaster MM 5a MM 4a
F12b: Social Disaster on
? Smaller Scale 赤字がその会社を襲った
MM 8 MM 5b
The company {experienced; *suffered; went into} red figures.
(cf. Red figures {?attacked; ?hit; ?*seized} the company})
? 肺癌が彼を襲った
Hierarchical Frame Network (HFN) F13: Long-term He {suffered; was hit by} a lung cancer
sickness
of “X-ga Y-wo osou” (active) and F13,14: Suffering a MM 4b
(cf. Cancer {??attacked; hit; seized} him)
“Y-ga X-ni osowareru” (passive) F13,14,15: Getting Sick =
Suffering a Mental or
Physical Disorder
痙攣が患者を襲った
Physical Disorder
The patient have a convulsive fit
E: Personal F14: Short-term (cf. A convulsive fit {??attacked; ?seized him)
Disaster? sickness
F14,15: Temporal ?MM 4c
NOTES Suffering a Mental or 無力感が彼を襲った
Physical Disorder
• Instantiation/inheritance relation is indicated by solid arrow. He {suffered from; was seized by} inertia
• Typical “situations” at finer-grained levels are thick-lined. F15: Short-term mental (cf. The inertia {?*attacked; ?hit; ?seized} him).
• Dashed arrows indicate that instantiation relations are not disorder
guaranteed. L1 Level Situations 不安が彼を襲った
?
• attack is used to denote instantiations of A, B. He was seized with a sudden anxiety.
• assault is used to denote instantiations of B1. L2 Level Situations (cf. Anxiety attacked him suddenly}
• hit, strike are used to denote instantiations of C.
• Pink arrow with MM i indicates a metaphorical mapping: 暴走トラックが子供を襲った
Source situations are in orange. F08: Accident The children got victims of a runaway truck
(cf. A runaway truck {*attacked; ?*hit} children.)
6

7. 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.

8. 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

9. 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
9

10. 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

11. 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
11

12. 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

13. And when such attraction takes place
• (lexical) meanings {m1, m2, ..., mn} (mi = m(wi)) of the words,
{w1, w2, ..., wn}, 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.
13

14. A “Competitive” Theory of
Meaning Construction
— Defining a Framework —

15. 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* = {M1,
M2, ..., Mn}.
• Interpretation with the highest goodness of fit score
wins out (cf. Optimality Theory)
15

16. 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

17. How Competition Converges
”Winner” (Sub)frames Frame[1]
Frame Element[1]: ...
• Each “semantic unit” SU, not
Frame Element[2]: ... necessarily a word, within a
...
activates
activates Frame Element[n]: ... sentence “activates” a set of
SU[1] Definition: ...
activates (sub)frames independently.
activates
accomodates
Frame[j]
Frame[i]
activates • Evoked (sub)frames compete
SU[i] activates
Frame Element[1]: ... each other either by
Frame Element[2]: ... inhibits
activates
accomodates
...
Frame Element[n]: ... • “activation”
•
SU[n] Definition: ...
or “(lateral) inhibition”
inhibits
activates inhibits
inhibits
• Once competition settles
inhibits Frame[k] down, the (meaning of) SUs of
Frame Element[1]: ...
Frame Element[2]: ... the “loser” (sub)frames
...
Frame Element[n]: ... accommodate to the
Definition: ...
”Loser“ (Sub)frame(s)
(meanings of) “winner”
(sub)frames
17

18. 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

19. 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
19

20. 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

21. 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 C1, C2
conditions are similar to real words in C0
• 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
21

22. Subject NP PP denoting Harm- Transliteration (word-by-word
Situation ID Translation Original Example (in Passive form)
denoting Victim causer translation from Japanese)
F01: Power Conflict The President was attacked by an The President was assaulted by an
The President an assasin !"#$% &'( )*+,-
between Human Groups assasin. assasin.
an(other) armed A country was attacked by another A country was attacked by another
F02: Invasion a country ./0% 12345( )*+,-
country armed country. armed country.
F03: Robbery on larger A bank branch was attacked by a A bank branch was attacked by a
a bank branch a masked man 678% 9:;<=>( )*+,-
scales masked man. masked man.
F03: Robbery on smaller An old lady was attacked by a purse An old lady was assaulted by a purse
an old lady a purse snatcher !"?8% @A( )*+,-
scales snatcher. snatcher.
Passengers-by were attacked by a Passengers-by were assaulted by a
F04: Persecution,Violence passengers-by a lunatic man !"BC% DEFGH( )*+,-
lunatic man. lunatic man.
A woman was sexually assaulted a
F05: Rape, Sexually assault a woman a pervert A woman was attacked by a pervert. !"I% JKI( )*+,-
pervert.
F06: Preying animal attack;
zebras lions Zebras were attacked by lions. Zebras were attacked by lions. !"LM% NOPQRS( )*+,-
Predation
F07: Nonpreying animal !"TU% VWXYZW[=7( )
children wasps Children were attacked by wasps. Children were attacked by wasps.
attack, usually for defence *+,-
A family was attacked by a runaway A family got the victim of an accident by
F08: Accident a family a unrunaway truck ]^U_% ]`=Na( )*+,-
truck. a runaway truck.
F09: Natural disater on
a town gust of wind A town was attacked by gust of wind. A town was hit by gust of wind. bcdc% QVeW( )*+,-
smaller scales
F10: Natural disaster on
an area a hurricane An area was attacked by a hurricane. An area was hit by a hurricane. f7% gh=>( )*+,-
larger scales
F11: Epidemic spread a city influenza A city was attacked by influenza. A city was hit by influenza. !"?i% jk,lm( )*+,-
The stock market was attacked by a
F12: Social disaster the stock market a debacle The stock market was hit by a sharp fall. !">n% Do( )*+,-
sharp fall.
A man was seized by cancer; A man
F13: Long-term sickness a man cancer A man was attacked by cancer. !"pa% q'( )*+,-
suffered cancer.
F14,15: Short-term mental
an old man panic An old man was attacked by panic. An old man was seized by panic. !"rn% nstuC( )*+,-
disorder OR sickness
A man was seized by a sharp pain; A man
F14: Short-term sickness a man a sharp pain A man was attacked by a sharp pain. vwx% yzC( )*+,-
suffered a sharp pain.
F15: Short-term mental A young man was attacked by a strong A young man was seized by a strong
a young man strong jealousy !"8% {n=|W( )*+,-
disorder jealousy jealousy
NONE zebras a masked man Zebras were attacked by a masked man. Zebras were attacked by a masked man. bcdc% gh=>( )*+,-
Experiments

23. 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%#-(
The degree of Y's affectedness is greater than the
Victim 9 Y4!]6!n</n14%o)p/'(
individual scale.
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-(
Victim 12 Y itself might have invited X's activity on it. Y3X.!]-4.!Y.Pyz3&'(
23

24. Full sentences (Baseline)
Transliteration (word-by-word
Subject NP PP denoting Harm-
Situation ID translation from Japanese assuming that More Natural Translation Original Example (in Passive form)
denoting Victim causer
osou translates to attack)
F01: Power Conflict The President was attacked by an The President was assaulted by an
The President an assassin !"#$ %&'( )*+,-
between Human Groups assassin. assasin.
an(other) armed A country was attacked by another A country was attacked by another
F02: Invasion a country ./0$ 120( )*+,-
country armed country. armed country.
F03: Robbery on larger A bank branch was attacked by a A bank branch was attacked by a masked
a bank branch a masked man 34$ 5678( )*+,-
scales masked man. man.
F03: Robbery on smaller An old lady was attacked by a purse An old lady was assaulted by a purse
an old lady a purse snatcher ./9:$ ;<,=>( )*+,-
scales snatcher. snatcher.
Passengers-by were attacked by a lunatic Passengers-by were assaulted by a lunatic
F04: Persecution,Violence passengers-by a lunatic man ?4@$ ABCD78( )*+,-
man. man.
A woman was sexually assaulted a
F05: Rape, Sexual assault a woman a pervert A woman was attacked by a pervert. ./EF$ FGHI'( )*+,-
pervert.
F06: Preying animal attack;
zebras lions Zebras were attacked by lions. Zebras were attacked by lions. JKLK$ MNOP( )*+,-
Predation
F07: Nonpreying animal
children wasps Children were attacked by wasps. Children were attacked by wasps. QRS$ TUVWX( )*+,-
attack, usually for defence
A family was attacked by a runaway A family got the victim of an accident by
F08: Accident a family a unrunaway truck ./YZ$ []M^_( )*+,-
truck. a runaway truck.
F09: Natural disater on
a town gust of wind A town was attacked by gust of wind. A town was hit by gust of wind. ./`a$ bc( )*+,-
smaller scales
F10: Natural disaster on
an area a hurricane An area was attacked by a hurricane. An area was hit by a hurricane. ./de$ fc( )*+,-
larger scales
./gh$ NPijkPl7m4( )
F11: Epidemic spread a city influenza A city was attacked by influenza. A city was hit by influenza.
*+,-
a debacle (or a The stock market was attacked by a
F12: Social disaster the stock market The stock market was hit by a sharp fall. nohp$ nq7[a( )*+,-
downturn, sharp fall) sharp fall.
A man was seized by cancer; A man
F13: Long-term sickness a man cancer A man was attacked by cancer. ./@$ rF7sP( )*+,-
suffered cancer.
F14,15: Short-term mental
an old man panic An old man was attacked by panic. An old man was seized by panic. ./9@$ tu( )*+,-
disorder OR sickness
A man was seized by a sharp pain; A man
F14: Short-term sickness a man a sharp pain A man was attacked by a sharp pain. ./8F$ vw( )*+,-
suffered a sharp pain.
F15: Short-term mental A young man was attacked by a strong A young man was seized by a strong
a young man strong jealousy ./x'$ vyz{|( )*+,-
disorder jealousy jealousy
NONSENSICAL zebras a masked man Zebras were attacked by a masked man. Zebras were attacked by a masked man. JKLK$ 5678( )*+,-
24

25. Test sentences with a nonce word
for Victim
Subject NP PP denoting Harm- Transliteration (word-by-word translation
Situation ID Translation
denoting Victim causer from Japanese)
F01: Power Conflict between
Nonce Word an assasin ____ was attacked by an assasin. The President was assaulted by ___.
Human Groups
an(other) armed ____ was attacked by another armed
F02: Invasion Nonce Word A country was attacked by ___.
country country.
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;
Nonce Word lions ____ were attacked by lions. Zebras were attacked by ___.
Predation
F07: Nonpreying animal attack,
Nonce Word wasps ____ were attacked by wasps. Children were attacked by ___.
usually for defence
A family got the victim of an accident by
F08: Accident Nonce Word a unrunaway truck ____ was attacked by a runaway truck.
___.
F09: Natural disater on smaller
Nonce Word gust of wind ____ was attacked by gust of wind. A town was hit by ___.
scales
F10: Natural disaster on larger
Nonce Word a hurricane ____ was attacked by a hurricane. An area was hit by ___.
scales
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 ___.
A man was seized by ___; A man
F13: Long-term sickness Nonce Word cancer ____ was attacked by cancer.
suffered (from) ___.
F14,15: Short-term mental A man was seized by ___; A man
Nonce Word a sharp pain ____ was attacked by a sharp pain.
disorder OR sickness suffered (from) ___.
F14: Short-term sickness Nonce Word panic ____ was attacked by panic. An old man was seized by ___.
F15: Short-term mental
Nonce Word strong jealousy ____ was attacked by a strong jealousy A young man was seized by ___.
disorder
NONE Nonce Word Nonce Word ____ were attacked by ___. ___ were attacked by ___.
25

26. Test sentences with a nonce word
for Harm-causer
Subject NP PP denoting Harm- Transliteration (word-by-word translation
Situation ID Translation
denoting Victim causer from Japanese)
F01: Power Conflict between
The President Nonce Word The President was attacked by ___. ____ was assaulted by an assasin.
Human Groups
____ was attacked by another armed
F02: Invasion a country Nonce Word A country was attacked by ___.
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
a bank branch Nonce Word A bank branch was attacked by ___. ____ was attacked by a masked man.
scales
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;
zebras Nonce Word Zebras were attacked by ___. ____ were attacked by lions.
Predation
F07: Nonpreying animal attack,
children Nonce Word Children were attacked by ___. ____ were attacked by wasps.
usually for defence
____ got victimized of an accident by a
F08: Accident a family Nonce Word A family was attacked by ___.
runaway truck.
F09: Natural disater on
a town Nonce Word A town was attacked by ___. ____ was hit by gust of wind.
smaller scales
F10: Natural disaster on larger
an area Nonce Word An area was attacked by ___. ____ was hit by a hurricane.
scales
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.
____ was seized by cancer; ___ suffered
F13: Long-term sickness a man Nonce Word A man was attacked by ___.
(from) cancer.
F14,15: Short-term mental ____ was seized by a sharp pain; ___
a man Nonce Word A man was attacked by ___.
disorder OR sickness 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
a young man Nonce Word A young man was attacked by ___. ____ man was seized by a strong jealousy.
disorder
NONE Nonce Nonce Word ___ were attacked by ___. ____ were attacked by ___.
26

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Results

28. 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.

29. PCA Plot: President –
real and nonce assassin
man –
words for Victim,
sharp pain
man -
cancer old man -
children - panic
(condition C1), wasps
woman –
young man –
strong jealousy
pervert
measuring zebras -
lions
strengths of passengers-by –
lunatic man
situation country –
family –
runaway truck
evocation by armed country
Harm-causer- Text city - influenza
denoting nouns bank branch –
masked man
town – gust of
wind
stock market -
debacle
+ at the center is the
“neural” point, where NW- area -
hurricane
ga NW-ni osowareta (“NW
{was attacked by; was hit by;
suffered (from)} NW”) is
located.
29

30. PCA Plot: President –
assassin
real and nonce man –
sharp pain
words for Victim, children -
man -
cancer old man -
panic
(condition C1), wasps
woman –
pervert
young man –
strong jealousy
measuring zebras -
lions
strengths of passengers-by –
lunatic man
situation country –
family –
runaway truck
evocation by
armed country
Harm-causer- Text city - influenza
denoting nouns bank branch –
masked man
town – gust of
wind
stock market -
Arrows indicate debacle
correspondences between area -
nonce word ratings and real hurricane
word ratings
Thickness of arrows
encodes strength
30

31. city - man -
influenza cancer
children -
wasps
PCA Plot: zebras - lions
real and nonce old man -
words for Harm- panic country –
armed country
man – sharp
pain
causer (condition area – President
C2), measuring hurricane
woman –
–assassin
pervert
strengths of
town –
gust of wind passengers-by – bank branch –
family – lunatic man masked man
runaway truck
situation young man –
strong jealousy old lady – purse
snatcher
evocation by
Victim-denoting stock market –
nouns debacle
Compared to C1,
attraction is weak, and
long
31

32. city - man -
influenza cancer
children -
wasps
PCA Plot: zebras - lions
real and nonce old man -
words for Harm- panic country –
–armed
armed country
man – sharp country
pain
causer (condition area –
President
President
–assassin
C2), measuring hurricane
woman –
woman –
pervert
–assassin
passengers-by – pervert
strengths of
town –
gust of wind passengers-by –
lunatic man bank branch –
family – lunatic man masked man
runaway truck
situation young man –
strong jealousy old lady – purse
snatcher
evocation by
Victim-denoting stock market
stock market –
nouns –debacle
debacle
Compared to C1,
attraction is weak, and
long
32

33. 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
33

34. Conclusion

35. 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
35

36. 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

37. 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).
37

38. Acknowledgments
• Toshiyuki Kanamaru, Kyoto University, NICT
• Jae-ho Lee, NICT
• Masao Utiyama, NICT
38

39. Thanks for Your Attention
and Your Tolerance for My Not-So-
Good English

40. After-thanks Slides

41. 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
PC1: Intendedness of X
PC2: Seriousness of Y's suffering
41

42. Profiles for Victim Rating F08, F10
1 5
2 A family - a runaway truck
3 NS - a runaway truck 4
4
NS - a runaway truck
5 3
6
7 2
8
9 1
10 g. an
.
... ce
. it . X. e. ti.. .. it. d ie
. it .
h in p la on d by for ct. on on
gt um ter ty be iza 's a fect to ct
11 liv i
n sh rea ra t iv
i ize long tim or X af
X
aff
e
Yi s g me fo 's ac tim vic f se
12 is a si vic X of re X's y cau y X's
Y es na dX e by lity a
ep avoid a b
cte
dn he it e ob ted ssibi to pr Xm arm
13
ffe Y is t e in v
so
nt ar ge po nce ou
ld on d a h
14 a av ea nt e Yc it y
Y's th er ee f th ch
a t iv ffe
re
15 of igh som s b are o ood 's ac su
gree lf m d ha w g X Y
de tse Y ha Y sa da
16 he Yi wa Y ha
T Y
17
1 5
2 A town - gust of wind
3 NS - gust of wind 4
4
NS - gust of wind
5 3
6
7 2
8
1
9
g. n. . e. it . X. e. ti.. .. it. ie. it .
10 h in ma r .. lac on by for za c t. on o d ct on
ngt hu ate r a p tivity ed be mi 's a fect Xt e
11 liv i Yi
s gre e fo ac tim
iz lo n
g icti for
X f
's a caus
e
's a
ff
is a is fv
12 ss am d X's vic by X it y o are dX y yX
Y ne e n vite be l ep avoi ma b
13 ted is th to ted ssibi to pr rm
fec e in on targe p o e uld o n X a ha
af Y av s c o
14 Y's th er
ea en the chan Yc t iv
it y red
of igh som be of d ac ffe
15 e m as a re o 's su
re lf d Y h s aw go X Y
eg tse Y ha da
16 ed Yi wa Y ha
Th Y
17
42