selectional restrictions are based on semantic frames: a case

Selectional Restrictions 1

Running Head: FRAME-BASED DESCRIPTION OF SELECTION

Selectional restrictions are based on semantic frames: A case

study of the Japanese verb osou

Keiko Nakamoto

Affiliation: Graduate School of Education, Kyoto University Address: Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan

Kow Kuroda

Affiliation: National Institute for Information, Communication and Technology Address: 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan

Hajime Nozawa

Affiliation: National Institute for Information, Communication and Technology Address: 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan

Takashi Kusumi

Affiliation: Graduate School of Education, Kyoto University Address: Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan

Contact: Keiko Nakamoto #302, Residence-Nishijin 21, 590 Tougorou-cho, Kamigyo-ku, Kyoto, 602-8254 Japan. TEL/FAX: +81-75-415-2626

E-mail: [email protected], [email protected]


Acknowledgments

The first and the second authors listed have contributed equally to this

paper. This research was partly supported by the 21st Century COE Program (D-2,

Kyoto University) of the Ministry of Education, Culture, Sports, Science and

Technology, Japan. The authors would like to thank Toshiyuki Kanamaru for the

materials used in the experiments. Correspondence concerning this paper should be

addressed to either Keiko Nakamoto, Department of Cognitive Psychology in

Education, Graduate School of Education, Kyoto University

([email protected]), or Kow Kuroda, Natural Language Processing Group,

National Institute for Information, Communication and Technology

([email protected]).


Abstract

This study aims at proposing an empirical approach on the basis of Semantic

Frames in order to construct a representational model of selectional restrictions.

First, sentences containing the Japanese verb osou (which roughly translates into

attack) were manually analyzed by linguists. It was demonstrated that the

selectional restrictions of osou and the disambiguation of the verb were mutually

dependent, and that their meanings could be expressed as a form of situation-level

frames. Second, in the experiments，participants were required to choose as many

NPs as possible to fill the blank in sentence frames like “____ attacked the bank in

the capital city” (originally in Japanese). The results indicated that the contingency

patterns of the subject and the object NPs were completely consistent with the

prediction by the semantic frame analysis. These results suggest the need for an

appropriate theory linking linguistic expressions to semantic understanding.


Introduction

Selectional restrictions are well-known phenomena in linguistic and

psycholinguistic studies. However, they require explanation—neither empirical nor

theoretical studies have provided an overall picture that describes and explains

selectional restrictions (Resnik, 1994; Pustejovsky, 1995).

In general, traditional accounts of selectional restrictions implicitly

assumed that they are a set of constraints imposed by the verb, i.e., the head of a

sentence. More specifically, these approaches presumed that verb meanings are

directly tied to event knowledge, and consequently serve as an effective source of

semantic constraints in the interpretation of co-occurring words, especially if these

are their “arguments.” According to the most radical verb-based view, it is the verb

that conveys all the crucial information for sentence processing, including

information required for thematic role assignment. Many researchers are aware

that such a verb-centered view of semantics hardly agrees with real data; however,

most of the practical analyses and accounts for selectional restrictions made in

linguistics and related fields of research have not strayed very far from this view.

This is probably because it is a view long maintained due to its theoretical

compatibility with supposedly important general principles of grammar, such as the

(Extended) Projection Principle posited in Principles and Parameters Theory

(Chomsky, 1986), the Head Principle posited in Head-driven Phrase Structure

Grammar (HPSG) (Pollard & Sag, 1994) and so on. These are principles that

guarantee the principle of compositionality for semantics as well as syntax. If the

basic semantic properties of a sentence were not predicted from the properties of its

head, i.e., the verb, how can the interpretation of a given sentence be sound and

plausible? This is the question that most linguists have in mind.


Recently, however, several studies have shown that verb meaning alone is

insufficient to explain the set of phenomena related to selectional restrictions and

thematic role assignment (Altman, 1999; Kamide, Altman, & Haywood, 2003;

McRae, Hare, Elman, & Ferretti, in press; McRae, Hare, Ferretti, & Elman, 2001;

Vu, Kellas, Petersen, & Metcalf, 2003). For example, McRae et al. (in press) showed

that nouns evoke event knowledge and facilitate the processing of the following

related verb if they denote the agent, the patient involved in a specific event, or

even the location of the event. For example, the participants in their experiment

were able to name the verb performing faster when they saw it after the noun actor

than when they saw it after semantically unrelated nouns like waiter. In addition,

Kamide et al. (2003) revealed that the patient for a verb preferred by participants

varied as a function of the agent noun phrase (NP). In their eye-tracking

experiment, the participants looked more often at a picture of sweets when they

heard The girl will taste; on hearing The man will taste, they looked more often at a

picture of beer. These studies show that the role of nouns in selectional restrictions

is as important as that of verbs, and that the event knowledge evoked by nominal

items affects sentence processing in important ways.

While these are very insightful findings for understanding the mechanism

of sentence comprehension processes, it appears that an appropriate representation

model describing the relationship between linguistic expressions and the event

knowledge evoked by them remains elusive. For example, Ferretti et al. (2001)

suggested that verb meaning varies depending on what types of arguments it takes.

However, the question of how to “encode” the interrelationship among a given verb’s

meaning, co-occurrence restrictions on its arguments and modifiers, and event

knowledge evoked by any of these, was not entirely discussed.


As the above discussion suggests, it is of great importance to note that

knowledge of a particular event is evoked by a verb and its arguments for revealing

the relationship between linguistic expressions and event knowledge. As Kamide et

al. (2003) showed, while the verb taste requires for its object a noun phrase that

denotes an edible thing, what constitutes an edible thing depends on the type of its

subject, the agent of tasting: ants will be edible for anteaters but not for sharks. In

other words, even if verb meanings provide abstract, schematic representations of

actions or events, they would still not be sufficiently informative for imposing

realistic restrictions on the semantics of their arguments. Moreover, meanings of

verbs mutate quite easily, adapting to the “environments” that their arguments, in

part, comprise For instance, Gentner and France (1988) demonstrated that, when

presented a sentence in which a noun and a verb were semantically ill-matched (e.g.,

The lizard worshipped), people tended to alter, or “adjust,” the meaning of the verb

in order to make the sentence interpretable. Some readers might believe that

context-induced semantic mutation of verbs occurs only when people are presented

with a verb and noun pair that is extremely mismatched, and can therefore safely

be ignored as an exception. However, this is not the case: every verb always displays

a semantic change, no matter how subtle, as a result of its adaptation to a lexical

context. Consider the sentence A group of masked men attacked the bank. It clearly

implies that the robbers, “a group of masked men,” used violence (perhaps by using

guns) to attempt to steal money from the bank. In a very similar sentence, A group

of masked men attacked the military base, “a group of masked men” (if successful)

are understood to have aggressively occupied the base, perhaps for political reasons.

These informal observations suggest that verb meanings are highly

context-sensitive and even mutable. This implies that selectional restrictions are


not a phenomenon that would be properly treated as a lexical phenomenon; their

appropriate characterization would surpass any explanation that is based solely on

the semantics of a verb.

This suggests the need for developing a (desirably computational) model of

semantics that allows a description of the co-variational and co-compositional

semantics1, given a verb and co-occurring nouns. Such a semantics capable of

expressing semantic interdependency is precisely what is required for the

appropriate characterization of selectional restrictions.

Frame Semantics and its Extension

Clearly, it is difficult to coherently characterize selectional restrictions due

to co-variational nature of semantics. For this, we employed a theoretical approach

inspired by Frame Semantics (Fillmore, 1982, 1985) and Berkeley FrameNet

(Fillmore et. al., 2001, 2003). For illustration, we briefly present its basic tenets.

Frame Semantics (FS) is a linguistic theory proposed by Charles J. Fillmore.

Its aim is to provide a description of lexical meanings in terms of “semantic frames,”

a theoretical construct that has linkages to Marvin Minsky’s “frames” (Minksy,

1977).

FS posits that each word in a sentence “evokes” a particular semantic frame

and profiles some element or aspect of the frame. The integration of the evoked

frames constitutes the interpretation, or the “understood content,” of the sentence.

FS characterizes a semantic frame as a piece of worldknowledge that

represents or encodes a situation, scene, series of scenes, or an event comprising

these. It defines a semantic frame as an organization of a number of elements called

frame elements (FEs). FEs include event participants such as an Agent and a


Patient. Along with these basic participant class elements, other types of elements

are regarded as elements of a frame: the Manner in which a participant behaves,

the Reason and Purpose that of a participant, and the Time and Place at which an

event occurs.

Berkeley FrameNet (BFN) is an ongoing project that aims to provide a

large-scale database of semantic frames (for English) and annotated corpora, based

on the assumptions in FS. To build the database of frames, BFN collects lexical

items that evoke a particular frame, identifies them as “lexical units” (LUs) for that

frame, and provides a definition to the frame in terms of FS. To illustrate this, we

have provided the definition of the Attack frame below (Berkeley FrameNet project,

2005):

Attack

Definition:

An Assailant physically attacks a Victim (which is usually but not always

sentient), causing or intending to cause the Victim physical damage. A

Weapon used by the Assailant may also be mentioned, in addition to the

usual Place, Time, Purpose, Reason, etc. Sometimes, a location is used

metonymically to stand for the Assailant or the Victim, and in such cases,

the Place FE will be annotated on a second FE layer. (In the Web page, the

italicized words are originally in color.)

The italicized words are FEs composing the ATTACK frame. Among these,

Assailant and Victim are the core FEs, the most important ones for characterizing

the frame.


LUs are lexical or phrasal items that evoke a particular frame. BFN

identifies the following lexical items as LUs of the ATTACK frame:

LUs: attack, assail,,…

LUs are frame-evoking items. Verbs are regarded as the most important

among these and are sometimes called “governors” of frames because they provide

information for, or give names for, the frames evoked by them. For example, in the

sentence The man killed her with a gun, the verb kill tells us what occurs (i.e., who

did what to whom) and gives a name for the event that took place (KILLING or

MURDER).

The methodology of FrameNet appears very promising because the

structure of the semantic frame will be an effective means to describe the pieces of

world knowledge on which language comprehension is based and to which lexical

items are linked. For our purposes, however, it was necessary to obtain the detailed

semantics of the Japanese verb osou (which is polysemous, roughly meaning attack,

hit, and damage); this is because our research was conducted in Japan and one of

our goals was to provide a sample semantic analysis for Japanese based on FS in

order to illustrate its plausibility.

A project called Japanese FrameNet aims, according to its announcement,

at building a database of semantic frames for Japanese (Ohara, Fujii, Saito,

Ishizaki, Ohori, & Suzuki, 2003). But it is hard to believe now that the project

proceeded as planned: virtually nothing is available to us even three years after its

beginning: the semantic frame database has not yet been released in any form; any

serious result of text annotation for elements of semantic frames are published in


any form..

There is another, more critical problem: the granularity of the semantic

frames. The frames used in the BFN database proved to be too coarse-grained for

our purposes. Thus far, BFN has not provided a sufficiently detailed analysis of

semantic frames that could enable us to characterize the selectional restrictions (for

our target verb osou, for example) as co-variational semantics. Therefore, if the

Japanese FrameNet follows the BFN methodology, it is clear that the

characterization of the semantic frames relevant to osou will be inadequate for our

purposes.

The ATTACK frame cited above is responsible for the semantics of the verb

attack (also the noun attack), which corresponds to one of the senses of our target

verb, osou.

Semantic roles or FEs defined in BFN have two important features: first, as

shown in the definition above, the FEs identified in BFN are a more detailed,

verb-oriented concept than thematic roles such as Agent or Patient, which are

typically used in syntactic theories such as those of Jackendoff (1990) and Levin and

Rappaport (1991). Second, BFN definitions of semantic roles avoid reductionism;

they show as much or even more respect for the semantic aspects of language than

syntactic ones, thereby setting no priority on syntactic information over semantic

information. This is a desirable feature and is consistent with the results of studies

by McRae and his colleague (Ferretti et al, 2001; McRae et al., 1997, in press).

However, BFN-style characterization of semantic frames is not free of

problems; among the most crucial ones is that its definitions for frames are too

coarse-grained to achieve proper descriptions of selectional restrictions for a variety

of verbs. This problem can be illustrated by considering, for example, the detailed


semantics of attack. The above definition of the ATTACK frame is assumed to be

applicable to the class of events denoted by the verb attack. Thus, the events

denoted by the statements The robbers attacked the bank in the center of the city

and The lions attacked the impalas are appropriate instances of the ATTACK frame.

Therefore, on the one hand, the robbers realize the role of the Assailant and the

bank realizes the role of the Victim; on the other, the lions realize the role of the

Assailant and the impalas realize the (potential) role of the Victim. This is a correct

consequence. However, the background knowledge necessary for fully

understanding the two situations is clearly different. The robbers are a group of

gangsters who use violence and weapons for money, while the lions physically

damage the impalas for predation. Accordingly, (the) impalas cannot be Victims of

(the) robbers, (*The robbers attacked the impalas), while (the) lions cannot be

Assailants of (the) bank (*The lions attacked the bank in the center of the city). As

revealed by this contrast, it is necessary to have semantic frames identified at

finer-grained levels if we are to provide a precise description of the selectional

restriction phenomena shown in this manner. In other words, it is clearly desirable

for us to be able to specify semantic frames in much greater detail as long as we are

concerned with word sense disambiguation along with the selectional restrictions

imposed. This requires, as discussed above, the co-variational semantics of a verb

and its arguments. In addition, what is needed is to allow verbs to specify semantic

frames only at relatively more coarse-grained levels than nouns, as in the case of

the ATTACK frame in BFN. If this is right, semantic frames that account for the

meanings of sentences with a realized subject and objects possess much

finer-grained semantic specifications in terms of semantic frames.

Thus, to fulfill this requirement, the BFN methodology was extended and


slightly modified in our study. First, we decided to make no reference to the BFN

database. We then identified semantic frames that account for the entire range of

semantic variability of Japanese sentences in which osou is the main verb. This was

done independent of BFN since we expected to compare our definitions with the

BFN ones later. In this bootstrap process, we followed a fully bottom-up and blinded

procedure, which commenced with manual coding by linguists of sentences collected

from a Japanese newspaper corpus. The target included all the sentences in the

corpus whose main verb was osou.

In addition, the definition of semantic frames was slightly modified in our

study; it turned out to be restricted as compared with the one adopted by BFN. This

is because what we refer to as frames are, roughly speaking, restricted to

“situations,” while frames in BFN have a considerably wider denotational range. We

also posited a “template” for a frame. The template is a hierarchical organization of

semantic roles such as <<EFFECTIVE: what>, <GOVERNOR: do>, <what>,

<OBJECT: to what>, <MANNER: how>, <PURPOSE: for what>, <LOCATION:

where>, <TIME: when>, etc.

Third, we left out syntactic aspects such as Tense, Aspect, Voice, and

Modality from the description in order to concentrate on the semantic aspects of

phrases and sentences. (For details of theoretical aspects of our approach, see

Kuroda and Isahara, 2005.) The procedure adopted to specify the subsystem of

semantic frames for Japanese through corpus analysis is reported in the next

section.

Corpus Study

The corpus analysis methodology used in this study was based on the


semantic intuitions of linguists (specifically, the second and third authors of this

article) because there is almost no appropriate resource for describing meaning at

phrase and sentence levels. In this study, a Japanese verb osou was analyzed

because it has a variety of meanings.

The analysis proceeded as follows: (1) collecting all sentences containing

the verb osou from a newspaper corpus, (2) specifying the subject and object NPs of

the verb for each sentence, (3) identifying the semantic frame evoked by the verb

(and its arguments), (4) giving an appropriate name for the semantic frame and FEs

(in other words, semantic roles) composing the frame.

Linguistic data. Sentences that contained the verb osou were collected from

a corpus (Utiyama & Isahara, 2003). The corpus consisted of articles from a

Japanese newspaper that were published between 1989 and 2001. All sentences

were collected from the corpus, irrespective of the inflected forms of the verb; 413

sentences were collected in this manner.

Frame identification procedure. First, the subject and object NPs were

specified for each sentence that contained the verb. For example, for the first

sentence in Table 1, the NP the lions is the subject (i.e., the agent of attack) and a

herd of impala, the object (i.e., the patient).

Next, the semantic types of NPs were identified. “Semantic type” is a kind

of natural category labeling that can, in principle, be identified without referring to

the context of the NPs. For example, the semantic type of the lions is predator

animals. This kind of semantic analysis is typically found in ordinal thesauri like

WordNet (Fellbaum, 1998; Miller & Fellbaum, 1991).

-----------------------


Insert Table 1 about here

----------------------

The third and fourth phases were essential for this analysis. The third

phase was to identify the semantic roles of the subject and object NPs in each

sentence. Semantic roles correspond to BFN’s FEs, expressing lexical meanings that

are defined depending on the situation described by the sentence. For example, in

the sentence The lions attacked a herd of impalas, the semantic role of the subject

NP is predators and that of the object NP is prey. In order to comprehend the

concept of semantic roles, it might be helpful to compare the sentence with The lions

looked for a watering place. In this sentence, the semantic role of the lions is

searchers who are searching for a missing object. Semantic roles such as predator or

prey are dependent on the situation, represented as the preying attack frame. In

contrast, in the sentence The City Bank was attacked by three armed men, the

subject role is robbers and the object role is a storehouse of valuables in the robbery

frame.

In the fourth phase, the semantic frames that are instantiated by each

sentence were identified. The third and fourth phases were conducted in parallel

and in a circular manner. In other words, the analysts coded the situation that was

described by a specific sentence as a frame name, such as preying attack; at the

same time, they coded the semantic roles that the subject and the object NPs

realized in the understood situation. In some sentences, the coding of the semantic

role would precede that of the frame, especially in cases where the name used to

represent either the subject or object NP was the very label given to the semantic

role in the frame (e.g., robber for the robbery frame). For other sentences,


identification of the frame would precede that of the roles, especially in cases with

no lexical item denoting a semantic role directly. In this case, the analysts had to

decide which situation was described in the sentence based on their interpretation

of its contents As mentioned above, the analysis was based solely on the semantic

intuition of two linguists, without making any reference to the BFN database.

Results of the corpus analysis. On the basis of the corpus analysis described

above, 15 semantic frames were identified for the situations described by the

sentences containing osou. The frames are listed in Table 2.

-----------------------


----------------------

The meanings of osou correspond approximately to attack, hit, and damage in

English．The frames from F01 to F05 are defined as situations in which human

assailants bodily damage other human victims, or carry out violent acts for a specific

purpose. F06 and F07 are frames in which the Agent (Assailant) is animal(s) and the

Patient (Victim) is animate being(s). These frames (F01 to F07) are often expressed by

attack in English. The frames from F08 to F12 are defined as situations in which

incidental damage is caused to humans. In these frames, the subject NPs

(Harm-causers) are typically inanimate things, and there is no intention of causing

harm. F08 typically has a machine or manmade accident name as the Harm-causer and

a noun referring to human beings (either literally or metonymically) as the Victim. F09

and F10 are frames that express the damage caused by natural disasters to human

beings and their activities. Typically, F09 is realized by a subject NP that denotes a


large-scale natural disaster and an object NP that denotes a place or an area name. In

contrast, F10 is usually realized by a subject NP denoting a small-scale natural disaster

and an object NP denoting a small number of human beings. The F11 and F12 are

frames in which the name of a place or organization is a typical NP for the Victim. F11 is

the frame that typically has an NP that refers to an epidemic disease as a subject. In

contrast, F12 has the name of an (economic) activity or event as the subject NP. The

frames from F09 to F12 are typically expressed by damage or hit in English. F13–F14

are frames in which a single human being is harmed by a mental or physical illness.

The typical NPs for Harm-causer are names of diseases or negative emotions, while

those for Victims are nouns denoting a single human being. These frames also display

no clear intention of causing harm. For a comparison of these frames with BFN, see

Kanamaru, Kuroda, Murata, and Isahara (to appear).

It should be noted that the identification of the frames and the selectional

restrictions of osou are closely related. For example, if the subject of osou is an NP

denoting robbers (e.g., the robbery team), plausible NPs for the object are restricted to

those that refer to institutions or persons who possess valuables such as money or

jewelry. In general, subject NPs denoting animate beings tend to co-occur with object

NPs referring to animate beings; however, there are a few exceptions, such as the

robbery (F03) or the invasion (F02) frames, in which the object NP tends to be a phrase

that refers to an institute or a small place such as a bank or a military base. In most

cases, however, animate beings “attack” animate beings such as a group of humans or

animals. They hardly take an object NP that refers to a large place, like Asian countries,

as victims. In contrast, inanimate subjects take either animate or inanimate entities as

an object NP, depending on their scales. If the subject NP denotes something that is

relatively large (e.g., typhoon), the object NP for the verb tends to refer to a large place


or area (e.g., Kyusyu area). In contrast, when the subject NP denotes a relatively small

event, like sleepiness, that takes place in a single person’s body, it is likely to take an

animate being, particularly a human being, as an object.

Since these results were obtained by depending solely on the intuition of

the linguists, psychological experiments were conducted to confirm the validity of

the semantic frames (Nakamoto et al., in press). In these experiments, participants

were required to sort sentences containing osou based on their semantic similarity.

The results demonstrated that the clustering of the sentences by laypersons was

fairly consistent with the analysis of the linguists.

The results of the corpus analysis showed that there exists a contingency

between the specified semantic frames and the semantic types of the subject or

object NPs in a sentence denoting a specific frame. For example, NPs that can be

the subject in a sentence referring to the frame spreading of an epidemic are limited

to those that denote the name of an epidemic (e.g., plague) or those that contain the

name of the epidemic (e.g., spread of the plague). However, it is impossible to

completely specify the semantic frame with the subject or object NP alone. A simple

example is the following pair: The plague attacked his daughter and The plague

attacked the Asian countries. Although the two sentences have the same phrase as

their subject, the former refers to the frame getting sick and the latter to the frame

spreading of an epidemic. This example demonstrates that the senses of noun

phrases and verbs are dependent on each other.

These results suggest that the selectional restrictions that operate in a

sentence headed by osou must be described in terms of co-variational semantics,

given a set of arguments of the verb. More generally, it can be stated that the

semantic frame or situation determined interactively by all words in a sentence


restricts and narrows the combinatorial range of the arguments for a given verb.

This implies that assuming strict compositionality in semantic operations fails to

capture an important aspect of sentence meaning construction because it fails to

properly characterize the effect of “mutual semantic accommodation” among the

words in a sentence (Langacker 1987, 1991) and the effect of co-compositionality

that arises from them (Pustejovsky 1995).

However, there were some limitations in the corpus analysis. One of them

was the nature of the corpus per se—no corpus can contain all the possible linguistic

expressions. In order to overcome this, psycholinguistic experiments were

conducted in this study. In the next section, we will illustrate the experimental

materials that were created, based on the results of the corpus analysis.

Experimental Materials

In the following experiments, we asked participants to choose words or

phrases that could fill a blank in a sentence like O was/were attacked by ____ (O ga

_____ ni osowareta), where O was the object NP of the verb. Adopting this procedure,

the intuition of laypersons with regard to selectional restrictions can be estimated

more directly and systematically. Evidently, in order to obtain a precise estimation

of their linguistic intuition, appropriate preparation of the experimental materials

was required. The results already obtained from the corpus analysis were useful for

this purpose.

As described in the previous section, there exist typical semantic types of

the subject and object NPs for each semantic frame. For example, in a sentence

expressing the frame violence (F05), the object NP is typically a single individual or

a small group of people who are vulnerable to violence. In the case of osou, more


varieties of semantic types can serve as subject NPs than as object NPs. NPs

denoting humans, organizations, animals, natural disasters, or diseases can serve

the purpose of a subject. In contrast, semantic types that can serve as Victims are

typically limited to NPs denoting humans, animals, activities, or places.

Based on these observations, typical subject and object NPs were selected

for each semantic frame. Three frames were divided into sub frames because they

contained possibilities for further sub-clustering. The NPs chosen for the

experiments are shown in Table 2. In the table, materials are paired such that each

NP-verb pair naturally evokes each frame. While preparing the materials, we

avoided strongly lexically associated pairs, such as gangsters and gang boss, as

carefully as possible in order to make it possible for an NP to combine with two or

more NPs. It should be noted, however, that the possible combinations of the NPs

are not restricted to those in Table 2. For example, the subject NP a new type of

pneumonia can damage statesman as well as Asian countries. Based on this

flexibility of possible pairing, it was expected that the results of the experiments

will reveal a general pattern of the selectional restrictions of the verb osou. More

precisely, it was predicted that an argument that originated from the same frame as

that denoted by the sentence containing the other argument would be chosen most

frequently; further, that the other argument that, when inserted in the blank,

would make the sentence meaningful (i.e., could realize one of the semantic frames)

would also be chosen with some frequency.

For Experiment 1, the object NPs in Table 2 were embedded in the active

( ____ attacked O) and passive (O was/were attacked by ____) sentences. Eighteen

subject NPs were used in the multiple-choice items for the blanks. For Experiment

2, the subject NPs were embedded in the active (S attacked _____) and the passive


( ____ was/were attacked by S), and 18 object NPs were used in the multiple-choice

items.

Experiment 1: Subject NP Choice

In Experiment 1, the participants were required to fill the blank at the

subject NP position by choosing more than one of the words or phrases listed in

Table 2. The purpose of Experiment 1 was twofold: (a) to find the selection patterns

of subject NPs for each object NP and (b) to compare the response patterns in the

active and passive forms.

The first purpose is addressed to validate our claim that the selectional

restrictions are determined by semantic frames. If our claim is valid, we should find

consistent patterns in the subject NP choices that can be predicted by the semantic

frame analysis. The second purpose was supplementary: by comparing the choice

patterns in the active and passive forms, it was examined whether or not the

situation knowledge that is evoked by the object NP and the verb osou was different

for the superficial grammatical forms.

Method

Apparatus and procedure. Eighteen sentences in the forms of ____ attacked

O ( ____ ga O o osotta) (for the active condition) and O was/were attacked by ____ (O

ga ____ ni osowareta) (for the passive condition) were used as experimental

materials. The Os in the sentences were filled with the object NPs in Table 2.

Eighteen subject NPs served as the choice items for the blanks.

Separate booklets comprising four pages each were prepared for the active

and passive conditions. The first page included instructions for participants to fill

the blanks in the sentences by choosing words and phrases from among 18 items.


When a word or phrase appeared to be perfectly suited to the sentence, the

participants were required to mark it with a circle. If they felt that the word or

phrase would possibly suit the sentence but were not be completely confident, they

were required to mark it with a triangle. They were allowed to mark as many choice

items as they judged fit with both a circle and a triangle. Six sets of the sentence

and the corresponding 18 choice items (subject NPs) were printed per page on the

other pages of the booklets. The sentences were printed in a random order that

differed for each participant. The order of the 18 choice items was also randomized

for each sentence.

The experiment was conducted in three separate classrooms. The

participants received a booklet that contained 18 active or passive sentences, and

were instructed to complete the booklet at their own pace. It took about 20 minutes

for the task to be completed.

Participants. Thirty-nine university and college students participated in

the experiment; 18 of them were assigned to the active form and 21 to the passive

form. All were native Japanese speakers.

Results and Discussion

On an average, the participants in the active form condition chose 3.48

items (SD = 2.41) as “perfectly suitable” and 1.40 items (SD = 0.84) as “possible” per

sentence. In the passive form condition, the mean number of the items chosen as

“perfectly suitable” was 3.03 (SD = 2.11) and that of the items chosen as “possible”

was 0.65 (SD = 0.71).

Only perfectly-suitable responses were used in the following analysis

because it was better to measure the participants’ primary intuition than to use all

the responses. In order to identify a general tendency for the selectional restrictions


for osou, the responses were collapsed across all participants for each condition.

Table 4 shows the number of times that the subject NP was chosen for each sentence

containing an object NP.

--------------


------------

In general, as shown in Table 4, the participants chose subject NPs for

object NPs that were chosen from the same semantic frames more frequently than

most of the other items. The observed patterns were very similar for the two

grammatical forms.

To confirm these tendencies statistically, a log-linear analysis was

conducted on the frequency table by using HILOGLINEAR (for model selection) and

LOGLINEAR (for parameter estimation) procedures in SPSS2.

The analysis revealed that there was no significant effect of the

grammatical forms. The effect of the grammatical form had no significance, both on

the main effect and on the interactions with the other variables. The model selected

as the best presumed that the choice patterns of the subjects were different among

the object NPs. A summary of the hierarchical deletion steps of the factors is

presented in Table 4. The standardized effects estimated with the selected model

are shown in Table 3.

---------------



-----------------

The log-linear analysis confirmed the tendency described above: the subject

NP that matched the object NP in each sentence was likely to be chosen most

frequently. It was also revealed that the object NPs denoting non-human objects

(such as Asian countries or transportation firm) induced the participants to choose

non-human subject NPs. In contrast, when the object NP denoted humans, various

types of subject NPs tended to be chosen. However, the patterns of item choice were

different for the different types of human object NPs. The object NPs referring to

relatively weak persons (e.g., a mother and her children) prompted the participants

to choose the subject NPs that denoted animate things, while the object NPs

referring to relatively strong persons (e.g., effective manager) elicited inanimate

subject choices (tumor).

A correspondence analysis was conducted in order to obtain the overall

contingency pattern of the object and subject NPs. Figure 1 shows a

two-dimensional solution of the correspondence analysis. These dimensions

accounted for 29.3 % and 23.4 %, respectively, of the total inertia.

-----------------------------

Insert Figure 1 about here.

---------------------------

Figure 1 shows that the first dimension (horizontal axis) reflects the scale

of the damages. On the right side of the map are the object NPs referring to areas or

large-scale institutions (e.g., Asian countries) and the subject NPs referring to


inanimate or abstract entities (e.g., new type of pneumonia). In contrast, the object

NPs denoting animate beings (especially humans) and the subject NPs referring to

animate beings or an abnormality of the body and mind are plotted on the left. The

second dimension (vertical axis) can be roughly interpreted to reflect the distinction

between natural and manmade damages. On the lower half of the display, the

subject NPs denoting animals, diseases and natural disasters (e.g., lions, large

typhoon, and sleepiness) are plotted. On the upper side are the subject NPs relevant

to human activities (e.g., stock crash, mob).

It is notable that the correspondence analysis clearly demonstrates the

contingency of the object and the subject NPs. The type of object NP that was

embedded in the sentence restricted possible subject NPs, although the verb

contained in the sentence was the same across all materials. Moreover, all object

NPs presented in the sentences were potential victims of attacking or hitting events.

These results might be problematic for purely verb-based approaches to sentence

semantics because the pattern of the participants’ choice was hardly interpreted as

a set of constraints purely imposed by the verb. If we assume that the verb has

sufficient information for characterizing the contingency pattern of the subject and

object NPs, there is almost no difference between the purely verb-based approaches

and ours because such a characterization clearly demands a detailed knowledge of

world events. More importantly, such a modification of the verb-based approach

may result in assumptions of word meaning representations that are far from

realistic.

Therefore, we claim that it is more straightforward to consider that the

contingency between the subject and the object NPs reflects mental representation

based on the semantic frame. The participants chose the subject-object pairs that


instantiate the semantic frames identified through the corpus analysis. In other

words, the results imply that the participants chose the appropriate NPs by using

their knowledge regarding the types of Harm-causers that can inflict damage to

specific types of potential victims.

Experiment 2: Object NP Choice

Experiment 2, in contrast to Experiment 1, investigated the patterns of

object NP choices for sentences containing the subject NPs and osou.

Method

Apparatus and Procedure. The apparatus and the procedure of this

experiment were the same as those in Experiment 1, except that the subject NPs

were given in the sentences and the object NPs served as multiple-choice items.

Participants. Forty university and college students participated in the

experiment. Twenty of them were assigned to the active form and the rest to the

passive form. All were native Japanese speakers.

Results and Discussion

The collected responses were analyzed in the same way as in Experiment 1.

On an average, the participants in the active form condition chose 4.33 items (SD =

2.21) as “perfectly suitable” and 1.16 items (SD = 0.75) as “possible” per sentence. In

the passive form condition, the mean number of the items chosen as “perfectly

suitable” was 4.26 (SD = 2.07) and the mean number of the items chosen as

“possible” was 0.87 (SD = 0.77).

Table 5 summarizes the number of times that each object NP was chosen

for each subject NP. The object NPs that match the subject NPs in terms of the

semantic frames were frequently chosen, like in Experiment 1. However, there


appeared to be some differences between the results obtained from the two

experiments: compared with those in Experiment 1, the choices for some object NPs

(e.g., woman living alone) were selected more often in the passive form condition

than in the active form condition.

-------------


---------------

To test these tendencies statistically, a log-linear analysis was conducted

with SPSS. A hierarchical log-linear analysis selected the model containing three

main effects (grammatical form (Voice, V), subject NPs (Sub), object NPs (Obj)) and

first-order interactions (V X Sub, V X Obj, Sub X Obj), but without the second-order

interaction (V X Sub X Obj)3. The goodness of fit index (G2) of the selected model and

that of the others are shown in Table 6. The standardized effects estimated in the

model are also shown in Table 5.

---------------------


---------------------

Although the hierarchical modeling selected the model with interaction

between the grammatical forms and the other variables, there was no subject-object

pair that had significantly different choice frequencies between the active and

passive forms, as shown in Table 5. Therefore, we decided to conduct the following


analysis using data collapsed across the grammatical forms.

A correspondence analysis of the collapsed data was conducted in order to

understand the choice patterns of the object NPs for each subject NP. The first and

the second dimensions accounted for 38.9 % and 20.9 % of the total variance,

respectively. Figure 2 shows the map of the two-dimensional solution.

-----------------------------

Insert Figure 2 about here.

---------------------------

Figure 2 shows configurations that are very similar to those in Experiment

1; again, the first dimension differentiated the scale of the damage caused by the

attacking (hitting) events, and the second distinguished whether the damage was

caused by natural events or human activities. The similarity of the results in the

two experiments suggests that evocation of the frames depends mainly on the

combinations of the verb and its arguments, regardless of the surface grammatical

forms. This is consistent with the definition of semantic frames because they are

assumed to be units of semantic understanding, not grammatical understanding.

The left side of Figure 2, however, shows a difference from Figure 1.

Comparing this to the results of Experiment 1, Figure 2 does not differentiate

between the natural and artificial nature of the attacking or hitting events when

the damages are relatively small. The difference between the two experiments

might reflect the distinctiveness of the choice responses. In Experiment 1, the choice

patterns of the subject NPs were similar among the object NPs that had similar

attributes. For example, the subject-choice frequency for the NPs small country in


the Middle East, Kyusyu area, and Asian countries, which had attributes such as

[inanimate, place, etc], showed high correlations (.82, .85, .77, respectively). As a

result, the differentiation among the semantic frames was observed, as had been

intended when we prepared the materials (see Table 2). In contrast, the choice

patterns among similar object NPs did not show such extremely high correlations

(for example, the correlation between stalker and drug addict was .60). It may be

possible to account for this by positing that the discrepancy between the two results

was caused by the difference in the strengths of the connections linking the NPs to

the semantic frames. In other words, the constraints on the object NPs, imposed by

the combination of the verb and its subject NP, might be stricter than those on the

subject imposed by the verb and its object NP. However, this is merely a hypothesis

based on rudimentary informal observation. Further work is required to draw such

a conclusion.

General Discussion

The results from the two experiments revealed that the verb osou imposes a

set of constraints on the combination of subject and object. Moreover, the

contingency patterns obtained from the participants’ NP choices were fairly

consistent with the predictions of the results of the frame-based corpus analysis.

That is, subject NPs denoting animate beings tend to associate with object NPs

denoting animate beings or small-scale institutions, while inanimate subject NPs

could associate with both animate and inanimate object NPs. In addition, the

participants displayed choice patterns that were fairly consistent with the semantic

frame analysis by the linguists. In many cases, the most frequently chosen

argument was one that came from the same sentence that contained another


argument.

These results support our claims that selectional restrictions are based on

semantic frames rather than lexical restrictions imposed by the verb, the head of a

sentence. As described in the introduction, some studies have reported that a verb is

not the only source of information for selectional restrictions or thematic role

assignments (Kamide, Altman, & Haywood, 2003; McRae, Hare, Elman, & Ferretti,

in press; McRae, Hare, Ferretti, & Elman, 2001; Vu, Kellas, Petersen, & Metcalf,

2003). However, there appears to be no adequate approach to selectional restrictions

thus far; a general theory is yet to emerge.

The scope of the present study is clearly limited because it is based on

examples that rely on just one Japanese verb, osou. However, the study

demonstrates that a recognizable subset of selectional restrictions can be explained

quite naturally as “adaptations” of word senses to their “environments,” which are

represented as semantic frames, given that semantic frames specify units of human

situational understanding or schemas for (idealized) situations. As far as this

account is sufficiently natural and generalizable to a reasonable degree, it suggests

that an approach based on semantic frames can, and will, provide a general account

of selectional restrictions.

However, there is one caveat. Despite the promise it holds, such an account

requires an appropriate degree of granularity in semantic description, and it is

evident that existing definitions of relevant semantic frames in BFN are not

sufficiently fine-grained. This implies that adopting FS is insufficient to account for

selectional restrictions in general: it must be done at an appropriate level of

detailed world knowledge. This would make certain modifications to the BFN

framework unavoidable.


The results of this study, which indicate that selectional restrictions to the

arguments correlate with the sense disambiguation of a verb, are consistent with

Hare, McRae, and Elman (2004). Their study demonstrated that the correlation

between meaning and syntactic structure is based on the specific senses of verbs

and not on the verbs themselves. Although their research focused on a syntactic

subcategorization of English verbs, a correspondence between their results and ours

is obvious. That is, to reveal the relationship between the meanings and structure of

languages, it is crucial to consider semantics at a fine-grained, context-sensitive

sense level and not at a coarse-grained, verb-general meaning level. The corpus

analysis procedure we proposed in this article will provide a coherent means of

identifying senses that a verb realizes with its arguments in different contexts.

In addition to implications with regard to the relationship between

linguistic expressions and knowledge of events, our results suggest that the naive

compositional view of sentence meaning must be reconsidered. In general,

traditional approaches to linguistic meaning implicitly assume that sentence

meaning can be described as a composition of lexical meanings of words in a

sentence according to their positions in a syntactic structure, as Traxler, Pickering,

and McElree (2002) suggested. It is true that many theoretical and empirical

studies have claimed that there are some aspects of sentence meaning that cannot

be reduced to lexical ones (e.g., Goldberg, 1995; Pustejovsky, 1995). Nonetheless,

few empirically valid theories have been proposed that are able to deal with the

relationship between sentence and word meaning sufficiently. As a result, questions

such as how meanings at the sentence level should be linked to meanings at the

lexical level remain unresolved, and empirical studies have been conducted with no

theoretical background in this regard.


For example, studies on the comprehension of polysemous words (e.g.,

Frazier & Rayner, 1990; Klein & Murphy, 2001, 2002; Vu et al., 2003) focus on the

process involved in sense disambiguation pertinent to their contexts (i.e., the

co-occurring words). In these studies, however, detailed content of the contextual

effects on word meaning has not been treated directly, that is, there is no detailed

description of what exactly is implied by the term “context.” We suspect that,

beneath this shortcoming, contextual effects are implicitly assumed to be

one-directional, especially when considering the (preceding) contextual effect on the

recognition of target words in experimental settings. However, our results suggest

that the contextual effects on the comprehension of word meanings are more subtle

and complicated than previous studies have probably assumed implicitly. Words in

a sentence would play a contextual role for each other in order to disambiguate their

senses. Recently, Pickering and Frisson (2001) proposed a model that explains that

the recognition of polysemous words begins with the underspecification of multiple

senses, and disambiguation is achieved when related information is provided by the

following context. Although their claim is very insightful for the purpose of

revealing how language is processed, it is difficult to acquire a complete

understanding of the underlying mechanism without an appropriate description of

the connections between sentence-level linguistic expressions and knowledge of

situations and events.

In addition, the evidence presented in this study questions the basic

assumptions of the traditional (particularly verb-based) approaches to sentence

meanings. Our basic notion, that sentence (or phrase) meanings should be defined

as connections between combinations of multiple words and situation concepts,

implies that it would be invalid to reduce a whole sentence meaning to a


composition of individual word meanings. In other words, meanings at the sentence

level show Gestalt-like properties, which cannot be reduced to meanings at the

lexical level. This implication is notable because there is no semantic theory that

characterizes sentence meanings in this way. Although several studies have argued

that situation knowledge plays an essential role in sentence comprehension

(Ferretti et al., 2001; McRae et al., 1997, 2001, in press; Vu et al., 2003), they have

tended to assume that the activation of a situation concept will be attributed to a

single word such as a subject, object, or verb. The co-variational semantic view, at

least, has not been instantiated in the experimental manipulation. This study

cautions against this kind of reductionism and impresses upon us the need to

develop a framework that describes the nature of contextual effects in greater detail.

It is especially important to specify which parts of a linguistic expression work in

cooperation to achieve an appropriate disambiguation and to acquire a coherent

understanding of a sentence.

In summary, this study demonstrated that the contribution of frame-based

corpus analysis and the evaluation of its results by psychological experiments is a

powerful method of investigating phenomena related to sentence-level

comprehension, such as selectional restrictions. It will provide a new method to

clarify the linguistic and psychological aspects of semantic understanding.


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Footnotes

1 An interesting version of such co-variational and co-compositional semantics is

provided in Pustejovsky (1991, 1995); however, we require more refinement from a

psycholinguistic perspective.

2 Strictly speaking, the log linear analysis might not be appropriate because the

responses in the table were not independent of each other (Please recall that each

participant made multiple choices for each object NP). In this study, however, the

response patterns among the participants were highly similar. Therefore, we

decided to collapse the data across the participants in order to make the statistical

results simple and intelligible. In the analysis, Delta was set at .5.

3 The model containing the second-order interaction was saturated.


Table 1

Examples of the semantic frame analysis

Prior context Key verb Following

context

Arg NP Semantic

type

Semantic

role

Semantic

frame

The lions attacked a herd of impalas. Subject the lions animal

[+predatory]

Predator Preying

attack

Object a herd of

impalas

animal

[–predatory]

Prey

The City

Bank

was

attacked

by three armed

robbers.

Subject three armed

men

human

[+grouped]

Robber Robbery

Object the City

Bank

institution Storage of

valuables

Note. For ease of understanding, this table was created with examples in

English, using attack as a translation of the Japanese verb osou. However,

not all meanings of osou translate into the word attack.


Table 2.

List of the semantic frames of “x ga y o osou” (x attacked y)

Frame Example Translation

F01A: bouto ga keikan tai o osotta.

Denoting harm to y caused

by a conflict between x and y

(x’s intentionality is unclear) A mob (SUBJ-pp) police squad (OBJ-pp) attacked

A mob attacked the squad of police.

futari no boukan ga hoshuha no seijika o osotta F01B: Denoting harm to y caused

by a conflict between x and y

(x’s intentionality is clear)

two ruffians (SUBJ-pp) conservative statesman (OBJ-pp)

attacked

Two ruffians attacked the

conservative statesman.

F02: shigen ni toboshii kuni ga chuutou no shoukoku o osotta


by x’s invasion country with few resources (SUBJ-pp) small country in the Middle

East (OBJ-pp) attacked

A country with few resourcse

attacked the small country in the

Middle East.


F03: Sannin no otoko ga ginkou o osotta Three men attacked the bank.


by x’s robbery three men (SUBJ-pp) bank (OBJ-pp) attacked

F04: mushoku no otoko ga hitori gurashi no josei o osotta


by x’s committing rape unemployed man (SUBJ-pp) woman living alone (OBJ-pp)

assaulted

An unemployed man assaulted a

woman living alone.

F05A: toorima ga suumei no shougakusei o osotta


by x’s violence (y is chosen

as weak persons by x.)

murderer (SUBJ-pp) several elementary school students (OBJ-pp)

assaulted

A murderer assaulted several

elementary school students.

F05B: yakubutsu chuudoku no otoko ga tsuukounin o osotta


by x’s violence (wihtout clear

intention for choice of

victims)

drug addict (SUBJ-pp) pedestrians (OBJ-pp) assaulted

A drug addict assaulted pedestrians.

F06: Denoting harm to y caused raion ga inpara no mure o osotta The lions attacked a herd of impalas.


by x’s preying attack lions (SUBJ-pp) herd of impalas (OBJ-pp) attacked

F07: dokuhebi ga tozankyaku o osotta


by x’s non-preying attack poisonous snake (SUBJ-pp) hiker (OBJ-pp) attacked

A poisonous snake attacked the

hiker.

F08: bousou trakku ga soyakozure o osotta

Denoting harm to y due to

an unexpected accident x Out-of-control truck (SUBJ-pp) mother and her son (OBJ-pp) hit

A truck out of control hit the mother

and her son.

F09: oogata no taifu ga nihon o osotta A large typhoon hit Japan.


by a natural disaster x (on a

large scale) large typhoon (SUBJ-pp) Japan (OBJ-pp) hit

F10: toppu ga TV no repootaa o osotta


by a natural disaster x (on a

small scale) a gust (SUBJ-pp) TV reporter (OBJ-pp) hit

A gust of wind hit the TV reporter.

F11: Denoting harm to y caused Supein kaze ga ajia shokoku o osotta The Spanish influenza hit Asian


by the spread of an epidemic

x Spanish Influenza (SUBJ-pp) Asian countries (OBJ-pp) hit

countries.

F12: oogata no fukyou ga nanbei no kuniguni o osotta


by a social phenomenon x

(large scale) large recession (SUBJ-pp) South American countries (OBJ-pp) hit

A large recession hit the South

American countries.

F12: daikibo na ristora ga unsou kanren no kaisya o osotta


by a social phenomenon x

(small scale).

Corporate downsizing (SUBJ-pp) transportation company

(OBJ-pp) hit

Corporate downsizing hit the

transportation company.

F13: akusei no gan ga hatarakizakari no dansei o osotta


by a disease x

(non-temporary) malignant tumor (SUBJ-pp) man in his prime(OBJ-pp) attack

A malignant tumor attacked the

man in his prime.

F14: Denoting harm to y caused suima ga yukiyama sounansha o osotta Sleepiness attacked the man lost in


by a disease symptom x

(temporary)

Sleepiness (SUBJ-pp) climber lost in the mountain in winter

(OBJ-pp) attacked

the moutain in winter.

F15: fukitsu na yokan ga binwan no shachou o osotta


by a feeling x (temporary) Sense of foreboding (SUBJ-pp) effective manager (OBJ-pp)

attacked

A sense of foreboding attacked the

effective manager.

Note. The italicized phrases were used as materials in the experiments. (SUBJ-pp) and (OBJ-pp) denote the past

positions marking the subject and object phrases, respectively. Although some of these frames contain verbs other

than attack in the English translation (e.g., hit), it is natural for the Japanese to express the words in these frames

using the verb osou. The (pp)s denote post positions.


Table 3

Choice frequency of each subject NP as a function of object NPs given in sentences (Experiment 1)

Object NPs embedded in the sentences

Subject NPs

(choice items) F01A

: squ

ad o

f pol

ice

F01B

: sta

tesm

an

F02:

smal

l cou

ntry

in th

e M

iddl

e Ea

st

F03:

ban

k in

Tok

yo

F04:

wom

an li

ving

alo

ne

F05A

: ped

estri

ans

F05B

: prim

ary

scho

ol st

uden

ts

F06:

her

d of

impa

las

F07:

mou

ntai

n hi

ker

F08:

mot

her a

nd h

er so

n

F09:

Kyu

syu

area

F10:

hou

se

F11:

Asi

an c

ount

ries

F12A

: mar

ket

F12B

: tra

nspo

rt co

mpa

ny

F13:

man

in h

is p

rime

F14:

clim

ber l

ost i

n th

e m

outa

in in

win

ter

F15:

eff

ectiv

e m

anag

er

Total

16 8 2 8 1 9 5 1 1 6 1 2 1 4 9 2 1 5 82

15 9 3 10 5 11 6 0 1 6 1 4 1 2 4 2 1 5 86

F01a mob

5.54* 1.06 -0.17 3.78* -1.91 3.87* 0.28 -1.33 -1.43 0.38 -0.52 -0.14 -0.72 2.56* 1.76 -2.41* -1.32 -1.23 5.37*


7 6 2 3 11 7 7 1 1 9 0 2 0 0 4 4 1 6 71

3 7 0 5 13 6 8 0 1 7 0 2 0 0 1 3 1 7 64

F01b Two ruffians

1.90 1.44 -0.71 1.97* 3.81* 3.32* 2.61* -0.76 -0.70 2.69* -1.16 0.01 -1.26 -0.77 0.11 -0.35 -0.59 0.94 0.96

0 2 8 0 0 0 0 0 0 0 2 0 4 0 0 0 0 0 16

0 2 4 0 0 0 0 1 0 0 1 0 2 0 1 1 0 1 13

F02 country with

few resoures

-0.75 1.52 6.40* -0.50 -1.13 -0.55 -0.93 1.05 -0.48 -0.97 3.21* -0.60 4.80* 0.33 0.21 -0.42 -0.43 -0.63 -4.90*

7 11 0 14 9 10 12 1 2 11 0 9 0 0 7 6 1 10 110

7 13 0 11 15 11 13 2 5 14 0 5 0 0 7 6 2 12 123

F03 three men

1.82 2.40* -1.91 4.86* 2.30* 3.84* 3.25* -0.26 0.64 3.08* -1.48 2.34* -1.57 -1.08 1.96* -0.07 -0.80 1.47 3.38*

3 2 0 1 17 4 4 0 0 5 1 1 0 0 1 2 1 5 47

0 3 0 0 21 0 3 0 0 2 0 0 0 0 0 2 0 7 38

F04 stalker

0.49 0.57 -1.04 -0.34 7.90* 1.52 1.93 -0.76 -1.04 1.80 0.39 -0.51 -0.72 -0.22 -0.72 0.12 -0.22 2.68* -2.66*

7 6 0 7 10 14 11 0 0 11 0 4 0 0 7 8 1 8 94

8 7 0 4 8 12 12 0 0 13 0 2 0 0 2 5 0 7 80

F05a drug addict

3.38* 1.58 -1.53 3.05* 2.64* 5.85* 4.28* -1.25 -1.53 4.30* -1.10 1.03 -1.20 -0.71 1.79 1.61 -1.04 1.58 0.43


4 6 1 2 10 18 12 0 1 13 1 3 0 0 2 5 1 6 85

3 10 0 1 18 15 18 0 1 15 0 1 0 0 0 8 0 8 98

F05b murderer

0.82 2.16* -1.24 -0.23 4.42* 6.87* 5.44* -1.31 -0.71 4.96* -0.52 0.00 -1.27 -0.77 -1.24 1.44 -1.15 1.16 0.96

3 2 1 1 2 3 4 18 4 6 0 1 0 1 1 3 2 3 55

2 1 1 0 0 2 4 20 5 7 0 2 0 0 0 2 2 4 52

F06 lions

0.54 -1.33 -0.35 -0.98 -1.75 1.06 1.16 9.83* 2.96* 2.61* -1.00 -0.06 -1.09 0.41 -1.37 -0.53 0.92 -0.21 -0.38

2 3 2 1 3 3 6 9 14 9 1 3 0 0 2 4 6 2 70

2 1 2 0 2 7 7 9 13 9 0 3 0 0 0 2 4 6 67

F07 wild boar

-0.58 -1.61 0.14 -1.38 -1.32 2.25* 1.86 5.70* 6.42* 2.94* -0.63 0.69 -1.33 -0.83 -1.39 -0.92 2.77* -0.72 1.63

6 3 0 4 2 10 9 1 1 12 0 9 0 0 5 4 1 3 70

9 5 0 4 4 9 9 0 3 12 0 8 0 1 4 7 0 2 77

F08 truck out of

control

3.25* -0.12 -1.62 1.89 -0.83 4.62* 3.21* -0.79 0.26 4.24* -1.19 4.23* -1.29 0.10 1.59 0.82 -1.18 -1.49 1.13

1 0 14 3 0 0 0 3 6 1 18 11 15 1 4 0 5 0 82

0 0 9 1 2 2 1 4 6 1 20 14 12 1 2 1 4 0 80

F09 large typhoon

-1.67 -2.22* 5.97* 0.31 -2.09* -0.82 -1.99* 2.53* 3.41* -1.74 9.50* 6.11* 7.35* 1.00 0.58 -2.30* 2.67* -2.35* 0.88


1 1 3 1 1 2 4 4 16 4 4 13 1 0 2 1 10 1 69

4 2 7 1 3 4 4 4 19 7 6 16 3 0 4 2 9 3 98

F10 landslide

-0.84 -2.53* 1.78 -1.37 -2.31* 0.15 -0.44 2.03* 6.90* 0.28 3.19* 5.79* 0.40 -1.06 -0.40 -2.53* 4.52* -2.62* 4.33*

1 3 11 0 3 0 2 3 1 1 9 1 14 0 1 7 1 3 61

1 6 12 0 7 0 3 1 3 0 4 2 9 0 0 15 3 8 74

F11 new type of

pneumonia

-1.17 0.46 6.04* -1.56 0.70 -1.61 -0.36 1.18 0.45 -1.99* 5.14* -0.35 6.78* -0.72 -1.57 3.83* 0.60 0.60 0.56

0 3 9 11 0 0 0 0 0 0 1 0 13 18 8 1 0 10 74

0 6 7 14 1 0 0 0 0 0 0 0 10 19 11 1 0 10 79

F12a stock crash

-1.42 1.72 5.50* 7.16* -1.57 -1.21 -1.60 -0.87 -1.15 -1.64 0.21 -1.27 7.50* 10.41* 5.69* -1.09 -1.09 3.99* -1.78

1 2 3 8 3 0 0 0 0 2 2 1 2 4 13 12 0 1 54

3 2 1 7 6 0 1 0 0 0 0 0 6 3 13 12 0 4 58

F12b corporate

downsizing

0.27 -0.72 0.95 4.92* 1.08 -1.41 -1.59 -1.06 -1.35 -1.24 0.68 -1.02 3.59* 4.48* 6.85* 4.97* -1.29 -0.67 -0.86

1 6 4 0 3 0 0 1 1 1 2 1 4 0 1 12 1 12 50

0 8 4 0 5 0 3 1 1 1 1 1 3 0 0 15 1 15 59

F13 malignant

tumor

-1.25 2.73* 2.81* -1.35 0.79 -1.40 -0.65 0.41 -0.17 -1.21 1.42 -0.42 3.25* -0.51 -1.22 5.70* -0.05 5.01* -0.99


3 3 0 0 6 0 4 2 5 1 0 0 0 0 1 7 15 4 51

3 4 0 0 4 0 5 0 6 2 0 0 0 0 0 8 12 9 53

F14 sleepiness

1.71 1.04 -1.15 -1.17 2.00* -1.22 2.28* 0.77 4.27* -0.31 -0.73 -1.27 -0.82 -0.33 -0.90 3.52* 8.01* 2.49* -1.87

2 4 4 1 5 0 0 2 4 2 2 2 3 2 2 2 5 6 48

3 8 2 1 6 1 1 2 4 2 1 1 5 0 1 3 10 7 58

F15 sense of

foreboding

-0.21 0.88 1.00 -0.94 0.51 -1.50 -2.17* 0.85 1.82 -1.28 0.64 -0.69 2.75* 0.81 -1.01 -1.34 4.39* 0.64 2.22*

65 71 64 65 86 80 80 46 57 94 44 63 57 30 70 80 52 85 1189

63 94 52 59 120 80 98 44 68 98 34 61 51 26 50 95 49 115 1257

Total

1.12 5.42* -0.88 -0.79 5.33* -0.41 2.75* -2.60* -0.89 3.17* -3.29* -0.11 -2.51* -4.66* 1.01 5.79* -1.35 6.81*

Note. In each cell, the first line shows the number of participants who chose the item in the active form condition, and

the second line shows the number of those that chose it in the passive form condition. The third line presents the

standardized estimated parameters. The asterisks in the third line indicate that p < .05.


Table 4.

A summary of the hierarchical deletion steps involved in arriving at the final

model

Step Model df G2 p Term

deleted

∆df ∆G2 ∆p

1 (V, Sub) (V, Obj) (Sub, Obj) 289 145.21 1.00

(V, Obj) 17 21.57 0.20

2 (V, Sub) (Sub, Obj) 306 166.79 1.00

(V, Sub) 17 22.63 0.16

3 (Sub, Obj) (V) 323 189.42 1.00

(V) 1 1.89 0.17

4 (Sub, Obj) 324 191.31 1.00


Table 5.

Choice frequency of each object NP as a function of subject NPs given in sentences (Experiment 2)

Subject NPs embedded in the sentences

F01A

mob

F01B

two

ruff

ians

F02

coun

try w

ith fe

w re

sorc

es

F03

thre

e m

en

F04

stal

ker

F05A

dru

g ad

dict

F05B

mur

dere

r

F06

lions

F07

wild

boa

r

F08

truck

out

of c

ontro

l

F09

larg

e ty

phoo

n

F10

land

slid

es

F11

new

type

of p

neum

onia

F12A

stoc

k cr

ash

F12B

cor

pora

te d

owns

izin

g

F13

mal

igna

nt tu

mor

F14

slee

pine

ss

F15

sens

e of

fore

bodi

ng

Total

F01a 15 4 2 5 0 3 2 1 5 4 2 4 1 0 3 1 3 6 61

0.09 1.04 0.41 -0.92 0.17 -0.73 1.05 -1.08 0.03 0.29 0.24 0.63 -0.17 -0.14 -0.16 -0.95 -0.76 -0.13 -0.45

13 1 1 7 0 3 0 6 4 2 3 2 1 0 3 2 7 8 63

squad of police

-0.09 -1.04 -0.41 0.92 -0.17 0.73 -1.05 1.08 -0.03 -0.29 -0.24 -0.63 0.17 0.14 0.16 0.95 0.76 0.13 0.45


5.98* -0.54 0.68 1.25 -0.99 0.07 -0.82 -0.31 0.67 -0.01 -0.74 -0.90 -1.36 -1.27 1.86 0.07 2.09* 2.00* -0.37

F01b 10 10 3 11 4 10 3 4 3 4 1 2 5 2 1 12 6 9 100

-0.26 0.40 0.47 0.42 0.14 0.13 -0.57 0.49 -0.72 -0.65 0.64 -0.38 -0.83 0.41 -0.88 0.18 0.01 1.16 -0.21

12 8 2 8 6 6 5 6 5 5 1 3 8 1 3 8 9 7 103

0.26 -0.40 -0.47 -0.42 -0.14 -0.13 0.57 -0.49 0.72 0.65 -0.64 0.38 0.83 -0.41 0.88 -0.18 -0.01 -1.16 0.21

statesman

2.37* 1.65 0.35 0.75 2.55* 0.97 0.44 -0.68 -1.58 -0.73 -3.02* -2.83* 0.55 -0.87 -0.68 4.13* 1.86 0.24 6.87*

F02 1 0 13 0 0 0 0 0 0 0 15 2 14 11 1 2 0 1 60

0.56 -0.16 -0.87 -0.22 0.09 -0.39 -0.13 -0.48 -0.18 -0.28 0.86 -0.71 0.55 0.77 0.54 0.70 0.05 0.04 0.73

0 0 17 0 0 0 0 1 0 0 14 3 7 4 0 0 0 1 47

-0.56 0.16 0.87 0.22 -0.09 0.39 0.13 0.48 0.18 0.28 -0.86 0.71 -0.55 -0.77 -0.54 -0.70 -0.05 -0.04 -0.73

small country in

the Middle East

-0.90 -1.28 10.77* -1.60 -0.20 -1.31 -0.73 -0.92 -1.49 -1.30 7.94* 0.60 6.52* 6.37* 0.07 0.28 -0.97 -0.59 -4.27*

F03 12 4 1 14 0 10 0 0 1 9 1 1 1 11 12 1 0 1 79

0.38 0.29 -0.80 0.39 0.06 1.37 -0.16 -0.90 0.48 1.05 -0.19 -1.24 0.47 -1.33 -0.28 0.30 0.02 0.00 1.07

7 2 2 7 0 1 0 2 0 2 2 3 0 15 10 0 0 1 54

bank in Tokyo

-0.38 -0.29 0.80 -0.39 -0.06 -1.37 0.16 0.90 -0.48 -1.05 0.19 1.24 -0.47 1.33 0.28 -0.30 -0.02 0.00 -1.07


4.52* 0.63 1.15 4.05* -0.72 0.91 -1.26 -1.29 -1.72 1.66 -0.97 -1.04 -1.39 8.36* 7.58* -0.70 -1.50 -1.48 -1.97*

F04 5 18 1 18 20 16 20 7 4 9 1 2 6 1 10 10 12 13 173

-0.81 0.10 0.87 -0.24 1.09 -1.12 0.24 0.95 -1.60 -0.69 -0.76 -0.83 -1.03 0.76 0.50 -0.78 0.80 0.44 -1.09

10 20 0 21 22 20 22 10 12 12 6 5 12 0 9 13 14 18 226

0.81 -0.10 -0.87 0.24 -1.09 1.12 -0.24 -0.95 1.60 0.69 0.76 0.83 1.03 -0.76 -0.50 0.78 -0.80 -0.44 1.09

woman living

alone

-1.21 3.19* -2.09* 2.05* 7.35* 2.75* 5.53* -0.75 -1.66 0.31 -3.17* -3.74* -0.17 -2.28* 2.52* 2.98* 2.43* 1.03 12.28*

F05a 6 16 0 16 2 17 20 8 14 14 1 15 0 0 0 0 1 2 132

-0.53 0.86 0.15 0.15 0.76 -0.52 0.97 1.12 0.41 -0.24 -0.83 1.98 -1.27 0.07 0.13 -0.04 -0.25 -0.92 -1.01

11 13 0 17 2 18 16 11 14 16 7 7 3 0 0 0 3 8 146

0.53 -0.86 -0.15 -0.15 -0.76 0.52 -0.97 -1.12 -0.41 0.24 0.83 -1.98 1.27 -0.07 -0.13 0.04 0.25 0.92 1.01

pedestrians

1.78 4.50* -1.55 4.13* 0.67 5.37* 7.10* 2.33* 3.73* 4.70* -1.33 1.69 -1.82 -1.71 -1.78 -1.89 -1.03 -0.91 2.76*

F05b 6 13 0 15 1 17 17 11 14 15 1 12 6 0 0 3 7 1 139

-0.64 0.84 0.12 -0.35 -0.22 -0.39 0.23 1.42 0.62 -0.22 -0.29 1.24 -0.80 0.04 0.10 0.26 0.56 -1.72 -0.92

11 10 0 19 3 16 19 13 12 16 4 8 11 0 0 2 9 10 163

primary school

students

0.64 -0.84 -0.12 0.35 0.22 0.39 -0.23 -1.42 -0.62 0.22 0.29 -1.24 0.80 -0.04 -0.10 -0.26 -0.56 1.72 0.92


0.93 2.52* -1.82 3.31* -0.05 4.20* 6.37* 2.53* 2.44* 4.00* -2.40* 0.56 1.35 -1.98* -2.05* -0.45 2.02* -1.73 5.27*

F06 1 0 1 1 0 2 0 17 6 3 1 1 2 0 0 1 0 1 37

0.65 -0.09 -0.10 -0.24 0.16 0.72 -0.06 1.01 -0.75 0.49 -0.88 -1.03 0.98 -0.15 -0.08 0.42 -0.96 -0.70 0.38

0 0 1 1 0 0 0 17 8 1 5 3 0 0 0 0 2 3 41

-0.65 0.09 0.10 0.24 -0.16 -0.72 0.06 -1.01 0.75 -0.49 0.88 1.03 -0.98 0.15 0.08 -0.42 0.96 0.70 -0.38

herd of impalas

-0.94 -1.31 1.39 -0.69 -0.22 -0.58 -0.76 9.09* 4.29* 0.65 0.83 -0.06 -0.47 -0.51 -0.57 -0.10 -0.15 0.16 -4.43*

F07 1 4 0 8 0 2 2 5 17 1 5 20 5 0 0 1 10 12 93

-0.61 -0.65 0.15 0.96 -0.88 -0.98 -0.06 0.29 0.21 0.02 0.81 1.04 0.92 0.07 0.14 -1.50 1.13 1.55 -1.07

3 8 0 5 3 4 3 11 19 1 8 17 3 0 0 5 10 10 110

0.61 0.65 -0.15 -0.96 0.88 0.98 0.06 -0.29 -0.21 -0.02 -0.81 -1.04 -0.92 -0.07 -0.14 1.50 -1.13 -1.55 1.07

mountain hiker

-1.44 1.21 -1.37 0.67 -0.24 -0.65 0.34 1.97* 5.80* -1.93 1.06 5.54* 0.27 -1.53 -1.60 0.42 4.39* 3.09* 1.5596

F08 5 14 0 14 0 18 17 10 15 13 1 10 1 0 0 1 6 5 130

-0.87 0.05 0.08 -0.41 -0.73 -0.30 0.29 1.33 0.00 -0.92 -0.37 0.91 -1.15 -0.01 0.06 0.60 1.56 0.22 -0.52

10 15 0 17 2 15 17 11 16 18 4 7 4 0 0 0 3 7 146

mother and her son

0.87 -0.05 -0.08 0.41 0.73 0.30 -0.29 -1.33 0.00 0.92 0.37 -0.91 1.15 0.01 -0.06 -0.60 -1.56 -0.22 0.52


1.08 4.36* -1.61 3.60* -0.77 4.87* 6.70* 2.64* 4.05* 4.63* -1.97* 0.67 -1.42 -1.77 -1.84 -1.63 0.55 -0.16 3.29*

F09 0 0 2 0 0 0 0 1 2 1 19 6 10 0 2 3 0 1 47

-0.10 -0.89 -0.92 -0.18 0.12 -0.36 -0.10 0.39 0.93 0.43 0.61 -0.05 0.87 -0.19 -0.24 1.00 -0.63 0.10 0.59

0 1 4 0 0 0 0 1 0 0 22 5 4 0 2 0 1 1 41

0.10 0.89 0.92 0.18 -0.12 0.36 0.10 -0.39 -0.93 -0.43 -0.61 0.05 -0.87 0.19 0.24 -1.00 0.63 -0.10 -0.59

Kyusyu area

-1.36 -0.84 3.79* -1.60 -0.21 -1.32 -0.74 -0.28 -0.78 -0.85 9.99* 2.96* 4.58* -0.49 2.39* 0.52 -0.45 -0.60 -4.41*

F10 5 8 0 12 0 7 0 3 12 15 7 13 1 0 0 0 0 0 83

0.24 0.45 -0.15 1.27 0.07 0.97 -0.15 0.44 0.50 0.83 -0.15 -0.58 0.49 -0.24 -0.17 -0.34 0.03 -1.39 0.91

3 4 0 3 0 1 0 3 6 5 11 13 0 0 0 0 0 3 52

-0.24 -0.45 0.15 -1.27 -0.07 -0.97 0.15 -0.44 -0.50 -0.83 0.15 0.58 -0.49 0.24 0.17 0.34 -0.03 1.39 -0.91

house

1.54 2.91* -0.73 2.24* -0.61 0.72 -1.14 0.82 3.96* 4.51* 4.28* 5.59* -1.25 -0.89 -0.96 -1.07 -1.38 -1.19 -2.40*

F11 0 0 8 0 0 0 0 0 2 0 16 1 16 14 2 1 0 5 65

0.01 -0.02 -1.28 -0.07 0.23 -0.24 0.01 -0.31 0.44 -0.13 1.08 -0.51 0.88 1.05 0.48 -0.82 -0.49 1.41 -0.01

0 0 18 0 0 0 0 1 1 0 18 2 9 6 1 2 1 2 61

Asian countries

-0.01 0.02 1.28 0.07 -0.23 0.24 -0.01 0.31 -0.44 0.13 -1.08 0.51 -0.88 -1.05 -0.48 0.82 0.49 -1.41 0.01


-1.57 -1.51 9.18* -1.82 -0.42 -1.54 -0.95 -1.19 -0.41 -1.52 8.27* -0.76 6.93* 7.32* 1.27 1.08 -0.71 0.97 -3.39*

F12a 3 0 1 1 0 2 0 0 0 1 1 2 0 19 2 1 0 4 37

0.76 -0.10 -1.27 -0.25 0.16 0.71 -0.07 -0.40 -0.11 -0.35 -0.01 0.38 -0.12 -0.59 -0.17 0.42 0.12 0.57 0.41

1 0 4 1 0 0 0 1 0 1 2 1 0 20 2 0 0 3 36

-0.76 0.10 1.27 0.25 -0.16 -0.71 0.07 0.40 0.11 0.35 0.01 -0.38 0.12 0.59 0.17 -0.42 -0.12 -0.57 -0.41

market

0.66 -1.26 2.88* -0.60 -0.17 -0.51 -0.70 -0.89 -1.46 -0.11 0.06 -0.29 -1.19 11.80* 2.48* -0.03 -0.94 1.82 -4.71*

F12b 8 2 2 5 0 3 0 0 1 7 2 5 1 9 12 0 0 1 58

0.95 -0.33 0.27 0.52 0.09 0.89 -0.13 -0.86 -0.30 0.37 -0.53 0.29 0.51 -1.27 -0.49 -0.32 0.05 0.05 0.86

3 2 1 2 0 0 0 2 1 3 5 3 0 13 12 0 0 1 48

-0.95 0.33 -0.27 -0.52 -0.09 -0.89 0.13 0.86 0.30 -0.37 0.53 -0.29 -0.51 1.27 0.49 0.32 -0.05 -0.05 -0.86

transport company

2.28* 0.12 1.34 0.43 -0.61 -0.87 -1.15 -1.16 -1.17 2.19* 0.71 0.76 -1.26 7.81* 8.33* -1.07 -1.39 -1.30 -2.58*

F13 2 11 0 13 5 14 12 4 7 10 2 5 8 1 14 16 15 8 147

-1.64 1.09 0.11 0.28 -0.21 -0.43 1.07 0.30 -0.24 -0.33 0.31 0.63 -0.88 -0.47 0.63 -0.69 0.77 0.06 -1.06

9 7 0 12 11 13 8 8 9 11 4 4 14 2 12 19 18 13 174

man in his prime

1.64 -1.09 -0.11 -0.28 0.21 0.43 -1.07 -0.30 0.24 0.33 -0.31 -0.63 0.88 0.47 -0.63 0.69 -0.77 -0.06 1.06


-1.84 0.25 -2.17* 0.53 2.97* 1.97* 2.48* -1.40 -0.82 0.92 -3.03* -2.98* 1.24 -1.55 4.48* 5.69* 4.18* -0.18 10.69*

F14 1 1 0 1 0 1 0 2 7 0 3 13 4 0 0 1 12 13 59

-0.19 -0.61 0.18 -0.34 -0.60 -0.66 -0.51 0.20 0.00 0.03 0.96 0.80 -0.21 0.09 0.16 -0.02 1.21 1.72 -1.06

2 3 0 2 2 2 1 5 9 0 4 12 6 0 0 1 12 10 71

0.19 0.61 -0.18 0.34 0.60 0.66 0.51 -0.20 0.00 -0.03 -0.96 -0.80 0.21 -0.09 -0.16 0.02 -1.21 -1.72 1.06

climber lost in the

moutain in winter

-0.78 -0.34 -0.87 -1.23 0.16 -0.71 -0.83 0.65 3.39* -1.86 0.60 5.09* 2.29* -1.03 -1.10 -0.01 6.73* 4.94* -1.80

F15 10 12 2 14 4 12 8 7 3 6 1 3 6 9 1 16 8 13 135

0.28 0.35 0.66 0.69 0.06 -0.88 -0.47 1.42 -0.87 -0.49 -0.10 0.18 -0.82 0.61 -0.80 -0.46 -0.13 1.19 -0.80

10 11 1 10 7 13 12 7 6 7 3 3 10 6 3 16 14 12 151

-0.28 -0.35 -0.66 -0.69 -0.06 0.88 0.47 -1.42 0.87 0.49 0.10 -0.18 0.82 -0.61 0.80 0.46 0.13 -1.19 0.80

effective manager

0.68 1.51 -1.19 0.44 1.90 1.78 2.64* -0.70 -2.31* -0.72 -3.29* -3.48* 0.15 2.27* -1.33 5.46* 2.23* 0.87 11.66*

Total 91 117 36 148 36 134 101 80 113 112 80 117 87 77 60 70 80 96 1635

0.15 0.35 0.11 0.78 -1.05 1.88 0.11 -2.50* 0.50 1.18 -3.04* 0.27 0.49 0.55 0.21 1.23 -1.00 -1.31 -0.75

105 105 51 132 58 112 103 116 122 100 123 101 92 67 57 68 103 118 1733

-0.15 -0.35 -0.11 -0.78 1.05 -1.88 -0.11 2.50* -0.50 -1.18 3.04* -0.27 -0.49 -0.55 -0.21 -1.23 1.00 1.31 0.75


2.01* 1.39 -4.20* 3.97* -4.48* 1.58 -1.91 2.19* 3.11* 1.55 3.72* 6.15* 0.98 -3.18* -3.05* -2.54* -0.69 4.38*

Note. In each cell, the first and the third lines (in bold) show the number of participants who chose the item in the

active and passive form conditions, respectively. The second and the fourth lines (italicized) show the standardized

effects for the active and the passive forms, respectively. The fifth line also shows the standardized effects, collapsing

the effect of the grammatical form. The asterisks in the second, fourth, and fifth lines indicate that p < .05.


Table 6 Fitness index of the selected model (Experiment 2)

Step Model df G2 p Term deleted

∆df ∆G2 ∆p

1 (V, Sub) (V, Obj) (Sub, Obj) 289 198.18 1 (V, Sub) 17 44.64 0.0003


Figure Caption Figure 1. Two-dimensional correspondence map of the subject NP choices (Experiment 1). The proximity of the words on the map reflects their contingency.


House

Transport company

Market

Asian countries

Kyusyu area

Corporate downsizing

Large typhoon

Squad of police

Mother andher son Primary school students

Pedestrians

Mountain hiker

Climber lost in themountain in winter

Man in his primeEffective manager

Statesman

Impalas

Bank in Tokyo

Small countryin the Middle East

Three men

Mob

Two ruffiansStalker

Murderer

Drug addict

Lions

Wild boar

Stock crash

Country withfew resources

Truck out of control Malignant tumor

New type of pneumonia

Sleepiness

Sense of foreboding

Landslide

Subject NP

Object NP

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0Dim 1

Dim

2


Figure Caption Figure 2. Two-dimensional correspondence map of the object NP choices (Experiment 2). The proximity of the words on the map reflects their contingency.


Transport company

Market

Kyusyu area

Large typhoon

Object NP

Squad of police

Mother and her son

Primary schoolstudents

Pedestrians

Mountain hikerClimber lost in themountain in winter

Man in his primeEffective managerStatesman

Impalas

House

Bank in Tokyo

Asian countries

Small country inthe Middle East

Three men

Mob

Two ruffiansStalker

Murderer

Drug addict

LionsWild boar

Stock crash

Country withfew resorces

Corporate downsizing

Truck outof control Malignant tumor

New type of pneumonia

SleepinessSense of foreboding

Landslide

Subject NP

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5Dim 1

Dim

2

(Choice Item)

(Embedded in sentences)

selectional restrictions are based on semantic frames: a case

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