Getting Deeper Semantics than Berkeley FrameNet with MSFA
Kow KURODA∗ , Masao UTIYAMA∗ , Hitoshi ISAHARA∗
∗ National
Institute of Information and Communications Technology (NICT), Japan
3-5 Hikaridai, Sorakugun, Kyoto, 619–0289, Japan
{kuroda, mutiyama, isahara}@nict.go.jp
Abstract
This paper illustrates relevant details of an on-going semantic-role annotation work based on a framework called M ULTI LAYERED / DIMENSIONAL S EMANTIC F RAME A NALYSIS (MSFA for short) (Kuroda and Isahara, 2005b), which is inspired by, if not
derived from, Frame Semantics/Berkeley FrameNet approach to semantic annotation (Lowe et al., 1997; Johnson and Fillmore, 2000).
1. Introduction
§1.1. presents the current status of our work, presenting our
framework called MSFA. §1.2. explains our motivations.
Main features of MSFA are presented in §2.1. Relevant
details of the proposed annotation scheme under MSFA are
presented in §2.3. with some explanations.
1.1. Current status
In (Kuroda and Isahara, 2005b), we defined a framework for fine-grained semantic analysis/annotation called
M ULTI - LAYERED / DIMENSIONAL S EMANTIC F RAME
A NALYSIS (MSFA for short), and produced a small
collection of semantic analyses/annotations for Japanese
texts taken from Kyodai Corpus (KC) (Kurohashi and
Nagao, 2003). This was just a first attempt to provide an
evaluation version released for free, expecting feedback
from users.1 More annotation work will follow.2
The target sentences of the official release were three
Japanese newspaper articles, comprising 63 sentences in total, selected from the KC. The English translation of the KC
was completed now at NICT, and the Chinese translation
is underway. We hoped that our semantic role annotation
could be used on a multi-lingual basis if a similar kind of
semantic annotation was added to other language’s part.
The number of the frames we identified through this annotaion/analysis work comes around 700 in terms of type.
Note that, as we will see later, the semantic analysis of a
sentence and the identification of frames needed for it are
performed at the same time, in a cycle. This distinguishes
our work from semantic annotation projects like SALSA
Project (Erk et al., 2003), which crucially depend on the
pre-existing (and yet completed) BFN database of frames.
In MSFA, we annotate the texts for semantic roles differentiated from semantic types, an important distinction
proposed by (Kuroda and Isahara, 2005a) based on insights from Frame Semantics (Fillmore, 1985; Fillmore and
Atkins, 1994).3 While semantic types can be equated with
1 Data
access is restricted, however. Users need an account.
this “official” result, some additional semantic analyses/annotations are being done on a volunteer basis in the form of open development available at
http://www.kotonoba.net/˜mutiyama/cgi-bin/
hiki/hiki.cgi?FrontPage.
3 Roughly, the proposed distinction of semantic roles from semantic types corresponds to the distinction of “relational” cate2 Besides
natural kinds by and large and encoded in a thesaurus effectively, semantic roles can’t: they are situation-specific
concepts highly dependent on culture, seeming to be more
responsible for text understanding than semantic types.
1.2. Motivations: Dealing with “deeper” semantics
Semantic annotation has become a trend: it is being practiced in a variety of forms. To name just a few frameworks,
we have Berkeley FrameNet (BFN, henceforth) (Baker et
al., 1998; Fillmore et al., 2003), PropBank (Kingsbury et
al., 2002), SALSA Project and SemCor. For comparison of
some of the major frameworks, see (Ellsworth et al., 2004).
When it comes to the task of specifying “deeper” semantics useful to text understanding at realistic levels, BFN
looks most appealing because it tries, or at least promises,
to provide more specific semantic information than you can
get through specification of argument structures of predicates (this is what PropBank aims for) and more interesting
information than you can get through WordNet-based word
sense disambiguation (this is what SemCor aimed for).
By realistic, we mean it is capable of specifying semantic intuitions that reflect actual mental processing in human
brain. Shallow semantic analysis like word sense disambiguations at the level of SemCor is useful, but we still need
more to get into realistic text understanding, and without
semantic specifications at some reasonably deep levels, understanding of texts at some psychologically realistic levels
would be just a fancy, for the reasons specified below.
1.2.1. Need for finer-grained specifications: A case
study of a Japanese verb osou
(Nakamoto et al., 2005) conducted psychological experiments based on a detailed corpus-based analysis of the
Japanese verb osou. They analyzed carefully all uses of “Xga Y -wo osou” (active) and “Y -ga X-ni osowareru” (passive form) collected from a Japanese-English alignment
data (Utiyama and Isahara, 2003).4 giving the network
analysis specified in Figure 1. The experiments showed that
ordinary Japanese were able to easily differentiate at least
15 situations (F01–F15) specified at very concrete levels in
gories from “object/entity” categories made by Genter and her
colleagues (Gentner, 2005; Gentner and Kurtz, 2005; Asmuth
and Gentner, 2005). Also, Gentner’s “relational schema” category
seems to correspond to “(semantic) frame” in the BFN sense, and
“(idealized) situation” in our sense.
4 The result of annotation work is freely available.
More Abstract
More Concrete
L2 Level Situations
狼が羊の群れを襲った
Wolves {attacked; ?*assaulted} a flock of sheep.
F06: Predatory
Victimization
L1 Level Situations
MM 1a
A: Victimization
of Animal by
Animal
(excluding
Human)
スズメバチの群れが人を襲った
A swarm of wasps {attacked; ?*assaulted} people.
MM 1c
MM 1b
F07:
Nonpredatory
Victimization
B2: Victimization
of Human by
Animal
(excluding
Human)
A,B:
Victimization of
Animal by
Animal
F07b:
(Counter)Attack for
Self-defense
F07a: Territorial
Conflict between
Groups
MM 1e
E: Conflict
between
Groups
B0: Victimization
of Human by
Animal
(including
Human)
MM 1d
MM 2
MM* 2
サルの群れが別の群れを襲った
A group of apes {attacked; ?assaulted} another group.
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.
F01: Conflict
between Human
Groups
F01,02: Power
Conflict between
Human Groups
B1: Victimization
of Human by
Human, Crime2
B3c: F01,02,03:
Resource-aiming
Victimization
MM 11
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.
F02: Invasion
MM 10
MM 9
B3: Victimization
of Human by
Human based on
desire-basis,
Crime1
A,B,C,D,E (=ROOT):
Victimization of Y
by X
F04: Persection
B3b: Physical
Hurting =
Abuse
MM 8
F03a: Robbery
引ったくりが老婆を襲った．
A purse-snatcher {attacked; ?*assaulted} an old woman.
F03b: Robbery
三人組の男が銀行を襲った．
A gang of three {attacked; ??assaulted} the bank branch.
F03: Robbery
B3a: Physical
Hurting =
Violence
狂った男が小学生を襲った
A lunaric {attacked, assaulted} boys at elementary school.
F05: Raping
男が二人の女性を襲った
A man {attacked; assaulted; ??hit} a young woman.
MM 14
?MM 7b
MM 0
F09,10(,11):
Natural Disaster
D: Perceptible
Impact
?
C,D,E:
Victimization in
Unfortunate
Accident
F12: Social
Disaster
F12a: Social Disaster
on Larger Scale
MM 4a
MM 5a
F12b: Social Disaster
on Smaller Scale
?
F13: Long-term
sickness
Hierarchical Frame Network
(HFN) of “X-ga Y-wo osou”
(active) and “Y-ga X-ni
osowareru” (passive)
E: Personal
Disaster?
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 B3 (or 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 4b
F13,14: Suffering a
Physical Disorder
L1 Level Situations
?
L2 Level Situations
F08:
Misfortune
?MM 4c
F15: Short-term mental
disorder
肺癌が彼を襲った
He {suffered; was hit by} a lung cancer
(cf. Cancer {??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)
F14: Short-term
sickness
F14,15: Temporal
Suffering a Mental or
Physical Disorder
大型の不況がその国を襲った
A big depression {?*attacked; hit; ???seized} the country.
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 5b
MM 13
F13,14,15: Getting Sick
= Suffering a Mental
or Physical Disorder
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.
MM 3a
MM 3b
?
MM 7a
MM 6a
F11: Epidemic
Spead
?MM 12
C: Disaster
F10: Natural Disaster
on Larger Scale
?MM 6b
?
突風がその町を襲った
Gust of wind {?*attacked; hit; ?*seized} the town.
F09: Natural Disaster
on Smaller Scale
無力感が彼を襲った
He {suffered from; was seized by} inertia
(cf. The inertia {?*attacked; ?hit; ?seized} him).
不安が彼を襲った
He was seized with a sudden anxiety.
(cf. Anxiety attacked him suddenly}
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.)
Figure 1: Hierarchical Frame Network Analysis (HFNA) of the situations referred to by osou-sentences in Japanese. “L1
and L2 Levels” refer to the semantic classes “L1 and L2” defined in Figure 2.
Figure 1. To set up for this, they tried to specify the full
range of the verb’s uses in terms of Frame Semantics, coming up with the finer-grained situations (F01–F15).
The result reported in (Nakamoto et al., 2005), especially
the “frame-lattice” specified in Figure 1, can be used to indicate how much fine-grainedness is needed if we try to
annotate every use of osou.
Note that osou is a rather generic verb to denote a situation of victimization of a h V ICTIM i, Y , by a h H ARM CAUSER i, X. Its meanings range from “X { attack; assault } Y ” (e.g., A gang of three { attacked; ?assaulted }
the bank, which comes close the “core” sense of osou, A
purse-snatcher { attacked; ??assaulted } an old woman)
to metaphorical “X { hit; strike } Y ” (e.g., The hurricane
{ hit; struck } the city), to further metaphorical “Y suffer
(from) X” (e.g., The man suffered { a damage; from a serious disease }). For overview, the English translations of
the osou’s occurrences in the freely accessible component
of the JEAD are specified in Table 2.
BFN provides a higher-level description of several
coarse-grained classes for the situations specified in Figure 1. Relevant BFN frames are Attack, Cause harm,
Cause impact, but they turn out to be too coarsegrained, for the reasons specified below, as described
in (Kanamaru et al., 2005) in more detail.
An important point is this: (Nakamoto et al., 2005)
found that if activities have different purposes (includ-
ing “null” value), no matter how “similar” they are in
other respects, people tend to categorize/classify them
as “different situations”, and they apparently had no difficulty in performing it. This means something quite ironical
to most linguistic/lexicographic analyses: namely, the use
of the same verb (e.g., osou) to refer to different situations does not guarantee the sameness of the conceptualizations with and against which situations are described. Rather, (even) ordinary people have no problem in
understanding sentences employing highly “fine-grained”
differentiated conceptualizations, at least as fine-grained
as the levels of F01–F15. If this is true, it follows that
the granularity levels of BFN’s Cause impact are unsatisfactory, and it can be easily confirmed by the following fact. Cause impact doesn’t distinguish metaphorical and nonmetaphorical uses. For example, Taifuu-ga
Tokyo-wo osotta (‘The typhoon { hit; struck } Tokyo’) and
Bouraku-ga shijou-wo osotta (‘The downfall { hit; struck }
the market’) are not differentiated.
Furthermore, we can find yet finer-grained distinctions
in some nonmetaphorical cases of it. For example, taifuu
(‘typhoon’) and toppuu (‘a gust of wind’) have different selectional properties: Taifuu-ga sono mati-wo osotta (‘The
typhoon hit the city’) sounds natural but ??Taifuu-ga sono
otoko-wo osotta (‘The typhoon hit the man’) sounds unnatural. In contrast, ?Toppuu-ga sono mati-wo osotta (‘A gust
of wind hit the city’) sounds awkward but Toppuu-ga sono
112
(TOTAL)
L0 = Sub
L1 Level
L1
attack[+human(s)]: rob
4
7
10
attack[+human(s)]: rob: break into
attack[+human(s)]: rob: make off with MONEY
attack[+human(s)]: rob: hold up
attack[+human(s)]: rob: threaten
2
1
1
2
attack[+human(s)]
23
23
attack[+human(s)]: kill
attack[+human(s)]: assault
attack[+human(s)]: assault: raid
attack[+human(s)]: assault: shoot
attack[+human(s)]: assault: shoot, wound
attack[+human(s)]: assault: shoot; rob
attack[+human(s)]: assault: stab
1
9
1
3
1
1
3
1
10
attack[-human(s),+animal(s)]
7
8
attack[-human(s),+animal(s)]: kill
attack[-human(s),?animal]: assault[+metaphoric?]:
turn on
1
1
1
hit,strike: hit
3
8
hit,strike: rock
hit,strike: strike
hit,strike: pound
hit,strike: destroy: wreak on
hit,strike: destroy: ravage
hit,strike: roar through
hit,strike: sweep through
hit,strike: wrought devastation
hit,strike: IMPLICIT in: earthquake
hit,strike: IMPLICIT in: in PLACE
hit,strike: there is
1
2
2
1
1
1
1
1
2
2
1
hit,strike[+metaphoric, +human(s)?]: occur[=attack]
1
hit,strike[+metaphoric]: hurt
hit,strike[+metaphoric?]: hit
hit,strike[+metaphoric]: hit
hit,strike[+metaphoric]: paralyze
hit,strike[+metaphoric]: IMPLICIT in: shocks from
hit,strike[+metaphoric]: overtake
hit,strike[+metaphoric]: take a toll
hit,strike[+metaphoric]: besiege
hit,strike[+metaphoric]: engulf
hit,strike[+metaphoric]: occur
hit,strike[+metaphoric]: fall on
hit,strike[+metaphoric]: IMPLICIT in: in PLACE
hit,strike[+metaphoric]: IMPLICIT in: problems
hit,strike[+metaphoric]: IMPLICIT in: turmoil
1
2
5
1
1
1
1
1
1
2
1
1
1
1
suffer
3
suffer: IMPLICIT in: victim
suffer: be injured
suffer: feel pain
suffer: bring sorrow to people
suffer: feel anxiety
suffer: seized with
suffer: suddenly begin a SYMPTOM
suffer: experience attack[-human(s), +metaphoric]
1
1
1
1
1
1
1
2
English verbs that translate OSOU
Semantic
Classes at
Level 1
Resourcethreatenig
situations
L2
Semantic Classes at
Level 2
L3
Semantic Classes
at Level 3
51
Intended Harmcausation[+animate]
90
Cause oriented
39
Disasters = Harmcausation[-animate]
10
Sufferings = Harmexperience
10
Effect oriented
3
42
Lifethreatening by
human
9
Lifethreatening by
nonhuman
18
Natural
disasters
21
Social
disasters[+met
aphoric]
10
Sufferings
5
3
2
2
6
2
9
4
4
2
5
3
4
Figure 2: English verbs that appear in the translations of
the osou-sentences in Japanese-English Alignment Data
(JEAD) (Utiyama and Isahara, 2003)
otoko-wo osotta (‘A gust of wind gust hit the man’) sounds
fine. All of this suggests that taifuu is understood as a relatively large-scale disaster that happens to a relatively large
group of people, whereas toppuu is understood as a relatively small-scale disaster that happens to an individual.
The same is true of Cause harm and Attack but we
omit details here for the sake of space.
1.2.2. Need for more effective identification procedure
Based on the observation above, we can state the problem we are facing as follows: if BFN promises to provide
“deeper” semantics, how deep should it be? More plainly,
are BFN frames “deep enough”? For this matter, our answer is negative, because Cause impact, Cause harm,
and Attack, provided by BFN, are not detailed enough to
specify selectional properties we need to describe to reflect
human semantic intuitions. But how is this case, after all?
We believe that the frame identification procedure
adopted by BFN now is not adequate for the purpose of
serious, exhaustive semantic annotation/analysis of an
unselected text, no matter how reasonable it may look for
the purpose of building a relatively wide-coverage database
in a short time. More specifically, we believe that the adequate levels of granularity need to be “discovered” through
inductive exploration into real texts, unlike BFN’s topdown quick “scans” for frames.
There are more “general” problems that are not particularly applicable to BFN, however. Most seriously, no evaluation measures are defined to tell whether a given semantic description is “deep enough” and without such
measures, any promise of providing deeper semantics is
likely to be ineffective. This means that we definitely need
to define a measure for the goodness of frame identification procedure, which provides a “testbed” for evaluating
semantic analyses in terms of frames.
There are certain possible choices. At one extreme,
one can define as many frames as people can recognize
as “different situations”, based on the selectional properties for frame-governing verbs. In the case of Japanese
verb osou, (Nakamoto et al., 2005) showed, as mentioned
earlier, that ordinary Japanese were able to easily differentiate at least 15 situations specified at the most concrete levels (F01–F15) in Figure 1, depending on the nature of the Assailant (the Agent-class role of Attack),
Impactor (the Agent-class role of Cause impact) and
Harm causer (the Agent-class role of Cause harm),
especially in terms of their purposes. If their claim is adequate, it is implied that HFNA in Figure 1 is not “too detailed.” If you want to claim it is, you need justify it on
empirical basis, because your claim argues against an empirical result from psychological experiments.
It is possible to say that BFN is more interested in establishing the syntax-semantics interface rather than doing
deep enough semantic analysis alone. It sounds like a good
excuse, but we are also afraid if it is not “too much for one,
not enough for the other.”
An important finding by (Nakamoto et al., 2005) is
that for osou-sentences, the semantic classes of subjects
co-vary with those of objects. For example, as F06:
Predactory Victimization in Figure 1 specifies,
a h P REDATOR i { attacks; ?*assaults; *hits } a h P REY i.
This is why The wolf attacked the (school of) sheep contrasts to ?*The wolf attacked the (school of) sardins, and
The tuna attacked the (school of) sardins contrasts to ?*The
tuna attacked the (school of) sheep. This piece of deeper
semantics, or knowledge of what animal serves as food for
what animal, enables us to make such judgments.
Arguably, the semantic co-variation (under coselection) among arguments (and adjuncts, too) for a
given verb can be used as a good test for differentiation
among finer-grained frames/situations, serving as a useful
distinctive feature applicable to semantic classification
of many other verbs. Later research confirmed this.
This means that we can use it as a guideline for frame
identification/differentiation. Unfortunately, however, this
crucial feature of semantic co-variation is largely ignored,
if not totally unrecognized, in BFN now, even though it
plays a crucial role in identifying frames/situations.
More importantly, if semantic co-variation accounts for
selectional properties of predicates, they ought to be specified somewhere, and we believe their specifications need
to be done in terms of frames/situations; or where else? —
Do you want it to appear in the entries for WOLF, SHEEP,
TUNA , and SARDIN , risking the notorious frame problem?
1.3.
Why MSFA?
The crucial point here is that there is a sort of “tradeoff” between the standardization and expressiveness measures: if you want more standardization, you usually need
to sacrifice expressiveness; if you want more expressive-
ness, you usually need to sacrifice its standardization level.
Obviously, BFN sets higher priority on the standardization
measure, but this is not the only way to go. In a sense, our
semantic annotation/analysis was done as an experiment to
see what would happen if expressiveness is more important.
This is why we did what we did.
2.
Details of MSFA-based Annotation
In this section, we specify relevant details of the MSFAbased semantic annotation/analysis to clarify what makes it
different from BFN-based one.
2.1. Features of MSFA
MSFA-based annotation has the following features:
(1) It is not presumptive, in that there are no presupposed
frames before doing annotation/analysis, except when
the results of a previous analysis are reused. More
specifically, frame identification in MSFA is necessitated by semantic analysis/annotation itself.
(2) This means that the analysis/annotation needs to be
greedy, in that as many frames as you need can be
identified and added to the analysis, as long as they
found necessary for providing deep enough semantic analysis/annotation of a text. Sufficiency is determined by successful specification of semantic covariations among arguments and adjuncts.
(2) It does not assume (at least currently) established definitions for any frames: they are always open to major
modifications, and the annotation task is designed so
to make it easy to manage.
(3) It is exploratory, in that it aims to “discover” frames
in a bottom-up, inductive fashion, through the process
of “exhaustive” semantic analysis of a text itself.
(4) It is meant to be exhaustive, in that every word and
multi-word unit are identified as a frame-evoking element: no exception. This means that you are not
allowed to ignore certain elements as “uninteresting
for our purposes.” Rather, you are required to seriously work with capturing the frame-evocation effects
by each word within a sentence in a running text.
(8) It is meant to be open, in that annotation work can be
done in the form of open-development. The annotation scheme was so defined that annotators are only
required to have a good command of Microsoft Excel,
dispensing with special house tools for annotation.7
The policy in (2) guarantees that MSFA’s aim to provide
“unrestricted” and “unbiased” description. It means that it
does not avoid the analysis of such “troublesome” cases as
metaphors, metonymies, idioms, and other figurative uses
of language. Analysis of them can, and it turned out to, be
pains-taking, but it’s worth doing it. We will be discussing
some relevant details in §2.3.3.
2.2. Remarks
One of the anonymous reviewers said that our first attempt, semantic annotations for only 63 sentences, was just
anecdotal. We don’t deny it, but we’ve just started. Please
be patient and hold your judgment until you see coming
releases. Any great developments started small.
For this, it should be noted that MSFA is designed for an
open, distributed development, and researchers are always
welcome to participate. It doesn’t depend on any house
softwares: annotators just need to work on Excel spreadsheets. This, we hope, would “compensate” our small start.
2.3. How Annotation/Analysis Goes in MSFA
2.3.1. Sample MSFAs
For illustration, a sample MSFA of (9), which is S-ID:
950107210-002 of the KC, is given in Table 3.
(9) アルゼンチンの元サッカー選手、ディエゴ・マラドーナ
氏が六日、同国の検察当局に一時身柄を拘束された。
(10) On January 6, Diego Maradona, a renowned former soccer
player of Argentina, was temporarily taken into custody by
the local prosecutor’s office in his country.
As mentioned earlier, MSFA was inspired by the Frame
Semantics/BFN approach. Details of MSFA, however,
were specified independently of BFN, for reasons specified
above. Their main differences are the following:
(11)
(5) In this sense, it is unbiased, in that identification of
frames is not motivated by any specific applications
like Machine Translation, QA, Information Retrieval.
(6) Also, it provides semantic analyses/annotations independent from syntactic parses, in that it only assumes tokenization, or “shallow parses” in some limited cases only for the sake of simplication. Unlike
many other frameworks, it tries to “unground” semantic specifications against syntactic ones. For one, specification of valence-patterns is out of our focus, because it is very likely that other databases will provide
such information independently.
(7) This makes annotation flexible, in that you are always
allowed to add or remove frames. If semantic analysis/annotation depended on certain syntactic parses, it
would be a disaster to do such an “editorial” job.
a. BFN provides standardized frames in the form of
a database, whereas MSFA provides an annotation database using frames discovered in a running text in an unselective way.
b. Frames identified using MSFA are more detailed
and specific, having more effectiveness than BFN
frames in terms of granularity.
This naturally has led to the difference in descriptive density between two frameworks. On average, a sentence has
20+ frames attached to it in the MSFA, drastically more
dense if compared to 2 or 3 frames per sentence in the BFN
annotation publicized at their Web site.8
Despite this, it should be noted that MSFA-based and
BFN-based annotations are expected to be compatible, because MSFA should give us a superset of BFN frames.
7 Of
course, the quality control of the annotation work is done
by a “committee,” managed independently using a Wiki site.
8 But BFN is not (yet) a project of semantic annotation per se.
So, it is kind of unfair to judge BFN from this point alone, as
correctly pointed out by one of the reviewers of this article. Saying
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Figure 3: MSFA of (9) [all annotations in Japanese]: Each column specifies a frame; each cell specifies a semantic role
with the corresponding segment in the first row as its realization value. Empty, uncolored cells indicate that segments6 have
no specific roles defined against the frames containing them. Relative positioning among frames has no specific meaning,
though organized to make the table easy to read.
Figure 4: Semantic Frame Network Analysis (SFNA) of (9) automatically generated by GraphViz (a free graphic-generator
software) based on the specification in Figure 3, i.e., the MSFA of (9): Each circle specifies a frame (all in Japanese)
2.3.2. Specifying the interrelationships among frames
Specified in Figure 4 is the relationship among frames that
constitute the semantic analysis of (9). It specifies how
frames are interrelated within a sentence. This is automatically generated from the MSFA in Figure 1.
Listed in (12) according to their relative frequencies
are “representative” relationships currently assumed in
MSFA,9 most of which have an equivalent, or analoguous
“frame-to-frame relation” in BFN:
this, it should be pointed out that there is one remaining problem
even if BFN is basically a lexicographic work: Who is going to
supplement a huge amount of “unknown” frames missing in
BFN, and how. We don’t believe it can be done automatically.
9 Other infrequent relations such as “F motivates G” are used.
(12)
a. “Elaboration” relation: A frame F elaborates another frame G; i.e., F inherits information from
G; e.g., h Murder i elaborates h Killing i.
b. “Constitution” relation: F constitutes G; i.e., F is
part of G. e.g., h Paying i constitutes h Buying i.
c. “Presupposition” relation: G presupposes F; e.g.,
h Buying i presupposes h Selling i.
d. “Presumption” relation: F presumes G. This is
the reverse of Presupposition; e.g., h Selling i presumes h Buying i.
e. “Realization” relation: F realizes G; e.g., h Buying i realizes h Obtaining i.
f. “Target/Transfer” relation: F targets G. This is
specifically introduced in MSFA to describe the
“figurative uses” of words including metaphor
and/or idioms.10 e.g., h Shooting i targets h Refuting i in sentences like He shot down the counterarguments from his opponents.
Some of the relations, such as Presumption, Realization,
Transfer do not seem to be defined in BFN.11
2.3.3. Encoding “figurative” senses
Real texts contain a lot of problematic cases in which words
have “nonliteral” meanings, to which the BFN frame definitions are not easily applicable. Representative, if not
all, cases are metaphoric expressions and idiomatic expressions. They pose a serious challenge to semantic annotation/analysis, because, as mentioned above, successful analysis of a metaphorical expression requires clarification of “(conceptual) metaphorical mapping” (Lakoff
and Johnson, 1999), and idioms have, for whatever reason, noncompositional, “superlexical” meanings encoded
in a distributed fashion. BFN does not explicitly specify
how to encode such effects. This is one of the major reasons we defined MSFA independently of BFN. Take He
spilled the political beans for example, where political interrupts spill(ed) the beans. This is an example of discontinuous frame-evocation. The multilayered, redundancytolerant design allows us to analyze this idiom with the
Target/Transfer relation, as in Figure 5. h P ROCECUTOR :
X i take h C RIMINAL : Y i into custody, appearing in (9)
in the passive form Y was (temporarily) taken into custody
and targeting h Arrest(ing) i (=h 逮捕 i) (via h Imprisoning i
(=h 拘留 i) realizes h Arrest(ing) i), is analyzed in this way.
Frame ID (Local)
F1
F2
presupposes
presupposes
Frame-to-Frame F3; unsatifies
F4; unsatisfies
Relations (Global) F3; elaborates
F4; targets F5
F2; targets F5
Frame Name
(Global)
Spilling
F3
F4
elaborates F4
Scattering
Holding
Keeping
F5
presupposes
F4; unsatisfies
F4; elaborates
F8
Leaking =
Failing to Keep
Secret
F8
Failing
*
~Characteriza
tion~
Politics
Characterizer
*
GOVERNOR
Spiller
Scatterer
Holder
Keeper
Leaker
spilled
GOVERNOR
GOVERNOR
OR EVOKER
EVOKER
EVOKER
EVOKER[1,3]
the
F7
Failed
Activity
He
political
F6
presupposes
F5,F7
Object.Attr[1] Object.Attr[1] Object.Attr[1] Object.Attr[1]
EVOKER[2,3]:
Secret.Attr[1] EVOKER[+c
omposite]
Object.Attr[2] Object.Attr[2] Object.Attr[2] Object.Attr[2] Secret.Attr[2]
beans
Object
Object
Object
Object
.
EXTENDER
EXTENDER
EXTENDER
EXTENDER
Politician[+pot
entially]
Failer
EVOKER[3,3]:
Secret
EXTENDER EXTENDER
Attribute:
EVOKER
EVOKER
Object
Issue
EXTENDER
EXTENDER
Figure 5: MSFA of He spilled the political beans.
3.
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