Journal of Robotics, Networking and Artificial Life, Vol. 1, No. 4 (March 2015), 270-284

Building Conceptual Dictionaries for an Integrated Narrative Generation System
Takashi Ogata

Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate, Japant-ogata@iwate-pu.ac.jp

Abstract

Integrated Narrative Generation System (INGS) is a framework for integrating our previous researches
on narrative generation. INGS integrates various narrative generation modules into an organic
architecture. This paper presents a verb conceptual dictionary and a noun conceptual dictionary to be
used in INGS. Each of the dictionaries has a conceptual hierarchy with single inheritance by is-a
relations. A terminal concept in the verb conceptual dictionary describes one or more case frame(s), theconstraints of the main elements and a sentence pattern. The basic description of each terminal concept
in the noun conceptual dictionary associates the concept to the other noun concept. The purpose of thestudy is not developing general conceptual dictionaries, but building them as a mechanism in narrative
generation, especially in INGS. Actually, we have combined the two developed conceptual dictionaries
with INGS. In this paper, we will present the basic mechanisms based on the cooperation of two typesof conceptual dictionary through an implemented narrative generation system, INGS.
Keywords: Narrative generation, Integrated Narrative Generation System (INGS), Noun conceptual dictionary, Verb
conceptual dictionary, Case frame, Constraint.

1. Introduction
Narrative generation or story generation as one of the
difficult research issues in artificial intelligence andcognitive science aims at generating narratives or stories
automatically by computer. Many systems have been
proposed in the field. An approach is by planning andgoal-plan strategy [1]. TALE-SPIN [2] is the pilot programand automatically generates different story texts according
to characters’ planning actions toward achieving specificgoals. DAYDREAMER [3] and MINSTREL [4] havemore comprehensive and complicated architectures basedon the above methods. The story generation system basedon an Aesop world [5] uses a goal-plan mechanism, as wellas large-scaled conceptual dictionaries to make using story
representation. Another main approach is grounded onstory grammar and story schema for narrative structuralformalization. GESTER [6] and Joseph [7], for example,
respectively consist of an interpreter with both a set ofstory grammar and a world model. On the other hand,
BRUTUS [8] shows a blended approach of planning andstructural methods. There is also third approach forinteractive drama or interactive story telling as anapplication of narrative generation. Oz project [9]developed an interactive drama with dialogue andactions in autonomous agents and Monfort [10]proposed an interactive storytelling system using amechanism for controlling narrative viewpoints.
We have also been conducting on research ofdeveloping narrative generation systems as intelligent
tools, in various meanings, for producing and creating
digital narratives and further literary texts [11]. One of
the main current research topics is to design anddevelop the Integrated Narrative Generation System(INGS) [12,13]. INGS integrates diverse narrative
elements into an organically integrated architecture
based on a variety of background conceptual ideas orconcepts relating to narrative generation, such as“expanded literary theory” [14,15] and “circulative
narrative generation” [16]. The expanded literary theoryis the conception of a new literary theory, on which
existing literary theories and narratology are used in thereconstruction for informatics. INGS actually applies
several literary theories to the important functions or

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implemented in INGS as functions including generatingbased on the mutual repetition between story generationand music generation [17].
The eventual goal of INGS is a versatile narrative
generation. We mean by the “versatile” the potential to
be used and applied to various purposes relevant tonarrative generation. The above BRUTUS system [8]deals with only a specialized narrative theme, “betrayal”,
while INGS intends to develop a more general mechanism
for various types of narratives based on a versatile
characteristic grounded on an integrated architecture.
Various intentional revisions of the structural techniques
and narrative contents knowledge in INGS will enable
the transformation of the genre and theme.
Comparatively large-scaled conceptual dictionaries are alsomechanisms for the versatile and general narrative
generation because of no having the limitation of previously
decided narrative themes and genres.
The objective of this paper is to introduce thepresent version of two conceptual dictionaries in INGS:
a noun conceptual dictionary and a verb conceptual
dictionary. These conceptual dictionaries respectively
have a hierarchical structure including terminal nodes tobe associated with basic or general language concepts,
such as “apple”, and intermediate nodes forsemantically combining the lower nodes including theterminal nodes, such as “fruits”. The main role is to give
semantic information to an “event concept” that is themost fundamental element in a narrative conceptual
structure generated by INGS. Narrative structuresgenerated by INGS in a generation process are divided
into conceptual structures and surface structures. The
surface structures include structures by language, music,
and so on, while the conceptual structures mean internal
semantic narrative representation to be expressed by thesurface structures. The conceptual structures are divided
into a story conceptual structure and a discourse
conceptual structure. An event concept or an event
simply is corresponded to a basic unit in their narrative
conceptual structures. An event concept basicallyconsists of a verb concept and several noun concepts.
The concepts are associated with the corresponding
elements and descriptions in the conceptual dictionaries.
As mentioned above, a group of conceptual dictionaries
in INGS is an important knowledge element forproviding some types of knowledge to each event

concept which is a basic unit in narrative conceptual
structures generated by INGS.
As papers relating to the conceptual dictionaries in
INGS, we have been presenting the idea and plan [18],the process of implementation [19], and the application
to a special issue [20]. This paper is a general overview
of the conceptual dictionaries in the present phase of
INGS as an explorative and incremental study.
Additionally, the basic part of conceptual dictionaries
for adjectives and adjective verbs has also beendeveloping and proper noun concepts and attribute
frames (using concepts) have been adding to the noun
conceptual dictionary. The truly comprehensive
systematization of conceptual dictionaries in INGS will
be addressed continuously in the future.
The story generation system by Okada and Endo[5], as mentioned above, uses general large-scaled
conceptual dictionaries in the story generation
mechanism, but is limited to only one narrative theme
and one narrative world. In contrast, INGS architecture
is constructed towards the versatility and generality ofgeneration through an organic fusion of diverse
narrative knowledge and techniques including planning,
discourse structure, story grammar, script, discourse
relation, and so on. And in contrast with the above
Aesop system is positioned as an application ofconceptual dictionary research, INGS pursues acomprehensive mechanism of narrative generation itself.
To develop conceptual dictionaries themselves is not our
extreme purpose. On the other hand, a story generation
system by McIntyre and Lapata [21] generates stories
by using a knowledge base about compositions of anevent sentence and chains of events extracted from anarrative corpus based on co-occurrence of words.
Stories are generated by a kind of tree search of possible
stories. This knowledge base is not a conceptual
dictionary and the system does not use general
conceptual dictionaries. In INGS, this kind ofknowledge for making chains or sequences of events isdefined as a relatively dependent group of knowledge,
story content knowledge base. INGS architecture ismore comprehensive and modular than many of theother narrative generation architectures.
Table 1. compares existing conceptual dictionaries
for the Japanese language. “Goi-Taikei, A Japanese
Lexicon” [22] is one of the largest Japanese lexical andconceptual dictionary for Japanese-to-English machinetranslation, which hierarchically organizes semantic

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attributes according to a single inheritance. We havereferred to this at the starting point to construct theoverall structures of our conceptual dictionaries. In
particular, as a way of refining and elaborating, we havereferred to “Japanese WordNet” [23] for the purpose ofcomplementing the shortage of intermediate concepts inthe noun conceptual hierarchy to a large degree. Insummary, on our noun conceptual dictionary, the basic
structure of the higher level basically has beenorganized referring to the “general semantic attributes
system (hierarchy) for nouns” in “Japanese Lexicon”,
while the hierarchy of lower intermediate concepts has
been defined referring to “Japanese WordNet”. And theterminal concepts basically use words in “Japanese
Lexicon”. However, these references are limited to thefirst developmental stage of the conceptual dictionaries.
We have been implementing an original organization of
the conceptual dictionaries according to the task of ournarrative generation. In particular, the specific form ofan event which is a basic unit in a generated narrative
structure is given by a case frame and the constraints inthe verb conceptual dictionary and the content
knowledge is provided by the noun conceptual
dictionary. The necessity in our narrative generation
incrementally will refine and elaborate the structures,organization, and contents of the conceptual dictionaries.
2. An Overview of INGS
In the macro design of our research, the architecture ofnarrative generation consists of conceptual generation
phase and surface representation phase. The former isdivided into the part of generating a story structure as
the narrative content to be narrated and the part ofgenerating a discourse structure as the structuraltransformation of a story structure. The surface
representation media include natural language, visualmedia, music, and so on. According to this framework,
we have been developing various mechanisms andmodules independently of each other. INGS synthesizes
these existing mechanisms in a same framework. A
variety of the modules are organically blended into awhole. The goal of this system is to execute thegeneration of narrative structures in some levels such as
story, discourse and surface representations in a unified
method. Fig. 1. shows the INGS architecture. The
system is consisted of several types of modules relating
to knowledge bases, narrative techniques for narrative
structures’ generation and transformation, and thecontrol. INGS is mainly implemented by Common Lisp
with 865 defined functions currently.
INGS has been developing through an exploratory
approach. In particular, we have been employing a
blended approach of both the top-down approach
relevant to the system’s macro design and the bottom-up
approach of designing and implementing each of themodules. And through such process, conceptual
dictionaries have been playing an important role for an
organized synthesis of some modules developed
independently. Standardizing the description of an event
form and the reference of common conceptual
dictionaries from various modules has been contributing
to the combination as a whole narrative generation
system. This is an important function of the conceptual
dictionaries from the viewpoint of the development
process of INGS.
Additionally, the “integrated” in INGS has thefollowing meanings: integrations of 1) narrative
generation phases, 2) expression media, 3) literary
theories and informatics, and 4) diverse techniques for

Table 1. Conceptual Dictionaries for the Japanese Language.
EDR Electronic Dictionary(National Institute of Information andCommunications Technology, 2001)[24]
About 410,000

The numberof concepts
The number About 270,000
of words
The structure Multiple inheritanceof concept
hierarchy
Bilingual dictionary, collocation dictionary,Featurescorpus, etc. are recorded

Japanese WordNet[23][25]

Goi-Taikei,A Japanese Lexicon[22]57,238About 3,000

93,834

About 300,000

Multiple inheritanceSingle inheritance

Words are divided into About 12,000 sentence patterns
groups by synonym
corresponding to the meaning of verb
(synset)concept are recorded.

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InputA set of generative parameters

OutputResults generated by each mechanism

Entire control

Story content
knowledge base
Event/statetransformation
knowledge base
Conceptual
dictionaries

Story control

Discourse control

Discourse
techniques

Story
techniques

Naturallanguagemechanism

Statemanagement
mechanism
StorymechanismMusicmechanism

Letternotation
dictionary

Image
mechanism

Image
data base

Discourse
mechanism

Function call

Data reference

Fig. 1. The Architecture of INGS.
narrative generation. INGS has a knowledge-basedarchitecture including story/discourse/expression phases
and several expression media of language/music/image.
Next, literary theories or narratology and informatics areintegrated through the property of expanded literary
theory or information design of narratology. The last 4)
means that the processing of each of the phases is
executed by the use of a variety of “narrative techniques”
which are equal to functions to actually constructvarious types of narrative structures.
A “story” generated by the story (generation)mechanism means the content of a narrative or atemporal sequence of events. A story is represented by atree structure consisting of the three basic units of event,relation, and state. An event is described with aconceptual representation form as a frame structure or acase structure including a verb concept and the related
noun concepts. A relation semantically combines eventsor the sub-structures. We have prepared a variety ofrelations such as “cause-effect”, “script”, etc. An event
is principally combined with a preceding state and asubsequent state [26]. A state is equal to the static
information in a story, while an event is corresponded tothe dynamic information. The chief functions of stateprocessing are to hold the knowledge about a story’s
world and to manage the coherency of a flow of events.
A story structure with events and relations is generatedusing one or more story techniques and states
associating with the events are made according toanother mechanism. Propp’s folktale theory of a story
structure [27] is also used in a part of the story
generation phase. This is an trial of our expanded
literary theory [28,29].
A “discourse” generated or transformed based on
the discourse mechanism means the structural aspect of
how to narrate a story. However, a discourse structure to
be narrated is distinguished from the surface media tonarrate or express it, and accordingly a discourse
structure is described with a conceptual representation
form. A discourse corresponds to the structure in which
a story structure is transformed to the form to be
narrated actually. The main part of discourse techniques
as a group of narrative techniques is currently defined[30] based on the narrative discourse theory by Genette[31] which is a typical narratological literary theory.
“Expression” means the aspect of narrative
representation using various surface media including
mainly natural language, such visual media as animationand still image, and music. The surface expression or
representation module consists of the following threemechanisms: a natural language mechanism thattransforms a story or discourse conceptual
representation into a sequence of sentences [32], an
image mechanism including an animated movie
generation mechanism [33] as a part, and a musicmechanism that conducts on the cyclical mutualtransformation between conceptual narrative structuresand musical structures [16,17]. In the present version of

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INGS, the natural language and image mechanisms arevery simple.
Various knowledge elements including conceptual
dictionaries are the other very important and essential
mechanisms for story generation especially. The
knowledge parts in INGS are strongly related togenerating narrative contents. The task of narrative
generation has both formal and substantial aspects froma point of view. In the story generation mechanism in
INGS, for example, story techniques are corresponded
to a formal aspect because each of the story techniques
is an abstract definition to be completed finally using
substantial knowledge. Main three types of knowledge
elements are dictionaries, a state-event transformation
knowledge base [26], and narrative content knowledge
bases. In the present version of INGS, in addition toconceptual dictionaries in this paper, there is adictionary for surface language generation calledlanguage notation (or language presentation) dictionary.
The state-event transformation knowledge base is used
when the story generation mechanism generates thestates according to an event concept generated. The
preceding and subsequent states are generated for an
event. The last narrative content knowledge bases,
ideally, should consist of some types of content
knowledge bases associating to narrative content
knowledge for story generation, music generation,
language generation, etc. In particular, the content
kno wledge for music generation and language
generation respectively has musical motives andfragmental sentences. But the present INGS does not
arrive at the stage and a story content knowledge base ispartially implementing. Each of story techniques is
associated with the story content knowledge which
defines the concrete fragmentary sequential structures of
events to be used in the corresponding to the story
technique. For example, for the “sequence” technique,many content knowledge structures will be able to be
defined as a part of the story content knowledge base.
Finally, INGS provides mechanisms for controllingthe overall generation level and the level of each of thephases in a narrative generation process. The major
function is to decide how to use narrative techniques(for story, discourse, and surface representation
including language and music) to direct characteristicsof the narrative texts to be generated. A relatively fullfledged control mechanism [30] is currently used inonly the discourse phase based on the literary theory by
Jauss (a type of reception theories) [34].
3. Basic Structures and Functions of the Conceptual
Dictionaries
As stated in the previous section, a story and a discourse
are mutually represented as a tree structure, and thebasic unit is an event or an event concept described as aconceptual representation form. Fig. 2. shows the tree
structure of a story and the actual example of an event(“An old person goes to a mountain from a home”). In
the story structure, each terminal node in the structure iscorresponded to an event, while each intermediate node
is a relation for binding the lower nodes. An event is
described as a case frame including a verb concept andseveral noun concepts. A case frame is defined using thefollowing eight basic cases: “agent” (the subject of anaction), “counter-agent” (the object of an action and aliving thing), “object” (the object of an action and a noliving thing), “instrument” (the tool used for an action),“location” (the place of an action), “time” (the pair of
the starting time and the ending time of an action),“from” (the starting place of an action) and “to” (theending place of an action). In addition to these basic
cases, nine kinds of optional cases are defined to treatsuch concepts which are not able to describe using theabove cases (Table 2.). As indicated in the table, a partof the optional cases is overlapped to the definition of
special cases by Okada [35] and Takeuchi [36]. The firstfunction of the conceptual dictionaries in INGS is todefine constituents of an event.
As the scale, the present version of the verb
conceptual dictionary has the following elements: 11951
verb concepts (terminal concepts), 36 intermediateconcepts and 16079 case frames. The present noun
conceptual dictionary has 115769 noun concepts and5809 intermediate concepts. Both conceptual
Cause-effectSequence
Event1

Event2

Repetition
Event3

Event4(event 行く3<Go> (type action) (ID 1) (time (time1 time2))(agent age%老人#1<Old_person>) (counter-agent nil)(location nil) (object nil) (instrument nil)(from loc%家#1<House>) (to loc%山#1<Mountain>)(adverb nil) (possessive nil) (situation nil) (purpose nil)(experiencer nil) (source nil) (idiom nil))Fig. 2. Examples of Story Structure and an Event.

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Table 2. Optional Cases.
Meaning

Sentence pattern

Okada(1991)

Takeuchi(2011)Name

1) Adverbial
concept

N1が N2を N3に N4 縮める(N1 shorten N2 by N4 to N3)

OC:complementof attributenumeral /correspondto adverb

adverb2) Possessive
case

N1が N2の N3に/へ 潜る(N1 go under N2)

3) Event

N1が N2を N3で 破る(N1 beat N2 at/in N3)

situation

situation4) Purpose ofactionN1が N2から/より N3へ/に/まで N4に 出かける
(N1 goto N3 from N2 for N4)

purposepurpose5) Experiencer

N1が N2に 疲れる(N1 get tird of N2)

experiencer

experiencer

6) Criterion ofcomparison

N1が N2を N3の 倍にする
(N1 make N2 twice as much as N3)7) Idiom

N1は N2(足)が N3から 遠のく(N1 go to N3 less frequently)

8) Information

N1が N2を N3に 書き取る
(N1 write down N2 in/on N3)

information

9) As

N1は N2としての 腕が 鈍る(N1 become les capable as N2)

asdictionaries respectively have a hierarchical structurefrom higher concepts to lower concepts according to“is-a” relations with the mechanism of single
inheritance. The conceptual dictionaries are all
described with Common Lisp as well as the main partsin INGS. Additionally, since intermediate verb concepts
in the hierarchy are only 36 in the present version, theelaborated selection of an element is impossible under ahierarchical range in event generation. We are
increasing the intermediate hierarchy.
A verb concept defines one or more “case frames”
which become template(s) of the event concept and has
the description of “constraint condition(s)” (simply“constraint(s)”) for limiting the range of each case’svalue. A constraint is defined using one or more noun
concepts existing in the noun conceptual dictionary andmeans the range that shows an element in a casecontained in a verb concept exits within the hierarchy ofthe noun conceptual dictionary.
If all the noun concepts in an event are within therange of the constraints, this mechanism will enable togenerate a natural and possible event like “A knight
fight an enemy”. However, if the noun concepts out of
the range are used, an unnatural and impossible event,
like “A knight fight a windmill”, may be generated. As
another example, in an event concept, “(eat (agent oldperson) (object rice-ball))” 1 (“an old-person eat a riceball”), when “(eat (agent N1) (object N2))” is defined as
the case frame corresponding to “eat” and the noun

possessiveOS:sourcecriterion-ofcomparison
usage

idiom

concept of “food” is defined as the constraint of “N2”,the noun concept to inserted into “N2” is thesubordinate concept of “food” in the hierarchy of thenoun conceptual dictionary. Next, a “sentence pattern”
is the third information which orders a basic sequenceof words based on the case frame to be utilized in thenatural language mechanism.

1
Though the concrete instances are actually used for the “old-person”
and “rice-ball”, we use the description for the sake of simplicity.

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A story

Two conceptual dictionaries
Verb conceptual
dictionary$Relation

Event

Action State

Physical-action Mental-action
Body-motion

Class<->Instance…

Verb-concept :eat

Sentence-pattern: “N1 eats N2”
Case-structure: ((agent N1) (object N2))
Constraint: ((N1 human -dead_person-personal_pronoun -demi_human)(N2 food -seasoning -drink/cigarette))

Noun conceptual
dictionary…

…

dwelling

Food

House

Apple

Human …
…

Abstract

Location Object

Subject

Fruit …

Man
Boy

・・・

(event Eat(time (time1 time2))(agent age%Boy#1)(object obj%Apple#1)(location loc%House#1))(time1Preceding(agents
state((ID age%Boy#1)(instance-of Boy@Man)(name Taro)(location loc%House#1)(possession obj%Apple#1)))(objects((ID obj%Apple#1)(instance-of Apple@Fruit)(location loc%House#1)))(locations((ID loc%House#1)))))

Noun
Physical$Relation

Event concept…

(time1Subsequent(agents
state((ID age%Boy#1)(instance-of Boy@Man)(name Taro)(location loc%House#1)(possession nil)))(objects((ID obj%Apple#1)(instance-of Apple@Fruit)(location nil)))(locations((ID loc%House#1)))))

Fig.3. Conceptual Dictionaries and Knowledge Structures of a Story.
Fig. 3. illustrates a relationship between verb andnoun conceptual dictionaries and the narrative
conceptual representation structures of a story in INGS.
The form of a story’s conceptual representation isequivalent to a tree structure in which a story ishierarchically described. Two or more events are
combined with a relation in a story’s tree structure. By arecursive processing, a structure to be combined with arelation sometimes includes two or more partial
structures with the other relation(s). Next, at the before
and behind of an event in a story, two states of aprecedent state and a subsequent state are added to store

such background information of the story as characters,
places, and objects. In addition to the above function,
when an event is generated, INGS automatically makes
the corresponding “instance(s)”. An instance is theconcrete, substantial or actual information of a noun
concept. The noun conceptual dictionary has a function
to make an instance from each general noun concept
described in a case frame according to the description of
the attribute frame of the noun concept. But in thepresent INGS, since the attributes frames is incomplete,
the mechanism of instance generation is tentative.
In summary, when the story mechanism generates

NounPhysicalSubject

Location

Abstraction

ObjectThing

Abstract_relation

Time

Place

Quantity

Form

State

Property

RelationGroup/systemExistence

Event

Nature_phenomenon

ActionHuman_
activity

MentalMatterLife

NatureArea

Institution

Organization

Human

Animal

Plant

Animal(Individual)
Beast

Animal(part)

Bird Amphibian/reptile Ocean_Life Bug

Sea_animalCarnivore

Ungulate

Dolphin

Whale

Orca

イルカ
(Dolphin)

Abstract_object

ドルフィン(Dolphin)

Anthropoid_ape
Walruswhale

RodentSeal

Animal(Individual(others))

Marsupial
Sea _ion

White_fin_whale

Bat

Beast
(legend)

Fur _seal

Beast(others)
Sea_animal

Livestock
Bakushia

Black_right_whale

Fig. 4. The Hierarchical Structure of the Noun Conceptual Dictionary.

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Ape_manDescription
format (a noun
concept)

Example (nounconcepthierarchy)(set [noun-concept]'([noun-concept](hierarchy(depth [number])(hype [superordinate-concept])(hypo [list of subordinate-concepts])(terminal [list of subordinate-terminal-concepts]))(frame [nil])))(set '獣[海獣]<animal[sea_animal]>'(獣[海獣]<animal[sea_animal]>(hierarchy(depth 8)(hype 獣<animal>)(hypo 獣[イルカ]<animal[dolphin]> 獣[鯨]<animal[whale]> 獣[シャチ]<animal[orca]> 獣[セイウチ]<animal[walrus]>)(terminal 海豹<seal> 海驢<sea_lion> 膃肭臍<fur_seal> 海獣<sea_animal> 儒艮<dugong>
バークシア<bakushia>))(frame nil)))(set '獣[イルカ]<animal[dolphin]>'(獣[イルカ]<animal[dolphin]>(hierarchy(depth 9)(hype 獣[海獣]<animal[sea_animal]>)(hypo nil)(terminal イルカ<dolphin> ドルフィン<dolphin>))(frame nil)))(set '獣[鯨]<animal[whale]>'(獣[鯨]<animal[whale]>(hierarchy(depth 9)(hype 獣[海獣]<animal[sea_animal]>)(hypo nil)(terminal 鯨<whale> 白長須鯨<white_fin_whale> せみ鯨<black_right_whale>))(frame nil)))(set '獣[シャチ]<animal[orca]>'(獣[シャチ]<animal[orca]>(hierarchy(depth 9)(hype 獣[海獣]<animal[sea_animal]>)(hypo nil)(terminal 逆叉<orca> 鯱<orca>))(frame nil)))Fig. 5. The Description Format of a Noun Concept and an Example of the Hierarchy.
an event concept, the verb conceptual dictionary
provides information of the corresponding case frameand sentence pattern. On the other hand, the noun
conceptual dictionary gives one or more noun concepts(general noun concepts and then the instances) to theevent according to the constraints in the case framefrom the verb conceptual dictionary.

noun conceptual dictionary 2 . The terminal nodes areequal to the most concrete concepts and the intermediate
nodes are corresponded to more abstract concepts tobind the lower parts. In Fig. 5., we show the description
format and an actual example and Fig. 6. means thehierarchical structure of the example. Each value of“depth” means the number from the highest level in the

4. Noun Conceptual Dictionary

2
Actually, the elements are described by Japanese. In the following
description relating to actual conceptual dictionaries, we will showeach concept as a pair of the original description by Japanese and theEnglish translation as necessary.

Fig. 4. is the overall structure of the hierarchy of the

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Sea_animal

Depth:8
・・・

Dolphin

Dolphin

Dolphin

whale

Orca

whale

White_fin_whale

Sea
_lion

9

Black_right

10

Seal

_whaleFig. 6. Hierarchical Structure of the Example in Fig.
5.
hierarchy.
As a chief principle for the development of thenoun conceptual dictionary, we use the terminal
concepts close to language level words as much as
possible because of the successive processing between
conceptual generation and surface language generation.
However, in Japanese, a word that has a meaning
ordinarily has several different notations. For example,
the word of “tanuki” that means “raccoon dog” hashomonyms of “狸” (“tanuki” in Kanji), “タヌキ” (“tanuki”
in Katakana), “たぬき” (“tanuki” in Hiragana), etc. The
generation process in INGS is divided into conceptual
generation and surface representation such as language
generation. A noun concept generated using the noun
conceptual dictionary is given an actual word representationor one of the notations using a word (or letter) notation
dictionary for surface language representation. We havedeveloped the dictionary in which various word notations
are described for each noun concepts.
There are two types of important knowledge-based
mechanisms relating to the noun conceptual dictionary.
Since the development has not finished in the present
version of INGS, we refer to only the basic concept anddesign for the future development here. As mentioned
above, each noun concept in an event generated isdefined as an instance in INGS. An instance is a proper
existence and is often able to be defined using a proper
noun. For example, “富士山<mount_Fuji>” is a proper
noun. On the other hand, a person’s name like “花子<Hanako>” is a name for a proper and concrete person.
Although “富士山<mount_Fuji>” and “花子<Hanako>”
are words, each of the instances needs to have a set of
information for the content. Firstly, proper nouns like
“富士山<mount_Fuji> are needed to be stored as a partof the noun conceptual dictionary. In particular, each
proper noun is linked to the corresponding general noun
concept as a proper noun concept. The essential
meaning of a “concept” is that it includes an “(noun)
attribute frame” which defines the content information.
We are currently adding proper noun concepts to thenoun conceptual dictionary.

The noun “conceptual” dictionary is necessary todefine attribute frames to represent the content of theinstance of each noun concept. The advanced processing
needs the fulfilling definition although an attribute
frame in the present version associating with a noun
concept mainly defines only the information about theposition in the noun conceptual hierarchy except
partialy completed definition and use. For example, wehave prepared a tentative list of attributes for narrative
characters by an analysis of novels, which includes
social, external, internal or psychological characteristics.
And we actually use a part of the elements in INGS.
Besides, we are beginning the study of the automatic
acuisition from various types of texts including novels
on the web.
The other main roles of attribute frames are as
follows. The first is to generate explanations anddescriptions of a noun concept and the group. The
mechanism is able to generate explanatory texts ofmainly abstract concepts of an instance such as social
characteristics of a character and descriptive texts ofexternal concepts such as a character’s face and body.
These functions are related to both the conceptual
structure’s construction of an explanation and thenatural language generation. The second role isespecially relevant to the part of natural language
generation by making a noun phrase using an attribute
frame for an instance. Figuratively speaking, if eventsand states in a narrative are equivalent to a trunk in thetree of a narrative, explanations and descriptions using
attribute frames are the nuts. As stated above, one of themost significant future tasks of INGS is tosystematically build attribute frames combined with thenoun conceptual dictionary.
5. Verb Conceptual Dictionary
Fig. 7. illustrates the overall hierarchical structure of theverb conceptual dictionary. As shown in the lower partof this figure using the example of “食べる 2<eat2>”, aterminal verb concept describes a sentence pattern, one
or more case frame(s), and constraint(s).
Next, Fig. 8. shows the description format of averb-concept and the example. The example includes
the following values: (1) A sentence pattern, “N1 eat
N2”. The “N1” and “N2” are corresponded to the valuesof “agent” and “counter-agent” or the values of “agent”
and “object” in case frames. “N3” is an optional value
and is not included in the sentence pattern. (2) Two caseframes, “(eat2 (agent N1) (counter-agent N2))” and“(eat2 (agent N1) (object N2))”. (3) Constraints for eachof the case frames. For example, the constraint for “N1”in the first case frame is “( 人 <human> - 死 人

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278

EventAbstract_
relationship

State

ActionMental_relationship

Natural_phenomenon

Possible

Causative

Begin

Physical_actionClose

Mental_actionThinking_action

Emotional_action

Sensory_action

Mental_transfer

Demise/destruction

Combining_action

Utilization

ResultGeneration

Physical_transfer

Change_of_body

Change_of_attribute

Possessive_transfer

Body_motion

Physical_state

Mental_state

Thinking_state

Sensory_state

Emotional_state

Causal_relationshipOwnership

Relative_
relationship

Attribute

Being

Case frames and constraints

食べる2<Eat2>(Eat (agent N1)(counter-agent N2))

Sentence pattern

N1がN2を食べる<N1 eat N2>(Eat (agent N1)(object N2))

N1: Human, –Dead_person, -Personal_pronoun, -Demi_human N1: Human, –Dead_person,
N2: Whale, Horse, Cattle, Pig, Goat, Sheep, Deer, Wild_boar,-Personal_pronoun,
Rabbit, Poultry, Game bird, Fish, -Fish(legend),-Demi_human
Octopis/squid/prawn/crab
N2: Food, -Seasoning, -Drink/cigaretteFig. 7. Hierarchical Structure of the Verb Conceptual Dictionary.
<dead_person> -人間〈人称<personal_pronoun>〉 -準
人間<semi_human>)”. The first case frame has threelists of constraint for “N1”, “N2”, and “N3”. (4) The listof the super-ordinate concept by “is-a” relation of theconcept. In this example, the super-ordinate concept is“(v 身体動作<body_motion>)”.
5.1. The validity of the constraints
Adequate case frames of each of noun concepts havebeen defined based on a sentence pattern and constraints.
We show an example of the definition based on asentence pattern, “N1 eat N2”. We simply hypothesizethat the constraints of “N1” and “N2” can respectively
be “(human, animal)” and “(food, animal)”. This meansthat “N1” takes the subordinate concepts of “human” or“animal” and “N2” takes the subordinate concepts of“food” or “animal”. In “N2”, “a living thing (animal)” isdistinguished from “a nonliving thing (food)” to define
the following two types of case frames, “(eat2 3 (agent
N1) (object N2))” and “(eat2 (agent N1) (counter-agentN2))”. The constraints of “N1” in the two case frames
are commonly “human” and “animal”, while theconstraints of “N2” are respectively “food” and“animal”. As mentioned above, a verb concept
sometimes holds some different case frames.
As shown in Table 2., in the present
3

implementation, case frames of 266 verb concepts aredefined using special nine types of cases instead ofbasic cases. We explain using the case of “purpose”. For
a verb concept of “go3”, the sentence pattern is “N1 go
to N3 from N2 for N4” and the constraint of “N4” is“(abstraction)” which shows the purpose of “go”. For
instance, “a man goes to a mountain from a house formowing”. If we interpret it as “to”, we are not able todistinguish “N4” from “N3” (“(place, field)”).
As a relating study, Okada [35] and Takeuchi [36]respectively proposed ten types of cases and moredetailed 71 types of ones to represent argument
structures of verbs which define the relationshipbetween a verb and nouns in a sentence. Since using the
all types may become too long and complex indescription, we tentatively use only nine types of caseswith “optional” sign.
Additionally, there are various semantic knowledge
resources relevant to our research. FrameNet [37] andJapanese FrameNet [38] define the meaning of a word using
a semantic frame which means the structured knowledge
about a typical scene. VerbNet [39,40] is a hierarchical verb
lexicon in which each class is described by semantic
predicates, thematic roles, and basic syntactic frames. These
resources and the above [36] are advanced semantic
knowledge to deal with complex narrative knowledge units.
In the modular approach of INGS, each common andrelatively large-scaled conceptual dictionary treats the

There are many concepts in “eat” and the “eat2” is the one type.

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Description
format (a verb
concept)

An example(set [verb-concept]'((name [verb-concept])(sentence-pattern [sentence-pattern])(case-cons-set((case-frame ((agent [*]) (counter-agent [*]) (location [*]) (object [*]) (instrument [*]) (from[*]) (to [*]) (adverb [*]) (possessive [*]) (situation [*]) (purpose [*]) (experiencer[*]) (source [*]) (idiom [*]) (information [*]) (as [*]))) ;; *Each required case takes
a “case number” described as ‘N’ + number like “N1” and “N2”. Unrequired cases
are “nil”.
(constraint (([N1’s constraint]) ([N2’s constraint]) … [Nn’s constraint]))) ;; A “constraint” isa list of noun concepts.
… ;; One or more pairs of “case-frame” and “constraint” are described.
)(is-a ([list of superordinate-concept]))))(set '食べる 2<eat2>'((name '食べる 2<eat2>)(sentence-pattern "N1 が N2 を 食べる"<N1 eat N2>)
(case-cons-set((case-frame ((agent N1) (counter-agent N2) (location N3) (object nil) (instrument nil) (fromnil) (to nil) (adverb nil) (possessive nil) (situation nil) (purpose nil) (experiencer nil) (source nil)(idiom nil) (information nil) (as nil)))(constraint ((人<human> -死人<dead_person> -人間〈人称<personal_pronoun>〉 -準人間
<semi_human>) (獣[鯨]<animal[whale]>] 獣[馬]<animal[horse]>] 獣[牛]<animal[cattle]>] 獣[豚]<animal[pig]>] 獣[山羊]<animal[goat]>] 獣[羊]<animal[sheep]>] 獣[鹿]<animal[deer]>] 獣[ 猪 ]<animal[wild_boar]>] 獣 [ 兎 ]<animal[rabbit]>] 鳥 [ 家 禽 ]<bird[poultry]>] 鳥 [ 猟鳥]<bird[game_bird]>] 魚<fish> -魚[伝説]<fish[legendary]>] たこ・いか・えび・かに<octopus・
squid・prawn・crab>) (場所<location> -交通路<traffic_route> -公共施設<public_facility>{その他<others>} -地域<region> -崖<cliff> 山<mountain>{部分<part>} 平地<plains> 岸<coast>) )))((case-frame ((agent N1) (counter-agent nil) (location N3) (object N2) (instrument nil) (fromnil) (to nil) (adverb nil) (possessive nil) (situation nil) (purpose nil) (experiencer nil) (source nil)(idiom nil) (information nil) (as nil)))(constraint ((人<human> -死人<dead_person> -人間〈人称<<personal_pronoun>>〉 -準人
間<semi_human >) (食料<food> -調味料<seasoning> -飲物・たばこ<drink・cigarette>) (場所<location> -交通路<traffic_route> -公共施設<public_facility>{その他<others>} -地域<region>-崖<cliff> 山<mountain>{部分<part>} 平地<plains> 岸<coast>) ))))
(is-a (v 身体動作<body_motion>))))

Fig. 8. The Description Format of a Verb Concept and the Example.
semantic knowledge which has a simple structure andanother mechanism handles more complex semantic
processing. In particular, state-event transformation
mechanism [26] generates the preceding and subsequent
states from an event using state-event transformation
knowledge base which defines transformation rules between
an event and the states. The above semantic resources willmore closely be connected with the part in INGS.
5.2. Editing constraints
A previous evaluation of constraints described in theverb conceptual dictionary indicated a difficult andambiguous result based on the standard of “actual
naturalness”. So, we have re-defined the constraints
according to the standard of “physical possibility” (inour daily life in common sense) as a basic policy.

Though the method pragmatically has problems, it isrelatively clear to define. For example, the purpose androle of constraints described in “A Japanese Lexicon”[22] is originally the conflict resolution for JapaneseEnglish translation. And the definition is too wide toadequately limit the semantic range of a noun concept tobe generated in the context of narrative generation. In
particular, in case of “eat2”, the subordinate concepts ofa constraint “food” corresponding to the “N2” include
inadequate concepts such as “drink”. One of the primarypolicies of developing constraints was to avoid such
ambiguity and inaccuracy.
We have defined constraints according to thefollowing two ways. The first is based on hyponymy
concepts. For example, “furniture” is substituted with“chair” and “bed”. The second is based on the partial
exclusion of hyponymy concepts. For instance,

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Copyright: the authors280“dead_person” is excluded from “human” using a minus
sign such as “-dead_person”. As an actual example, forthe constraints of “N3” in “N1 return from N2 to N3”,
we have defined “(lodging housing area -area(scope) area(human_activity) -land -world foot_of_mountain
mountain_pass valley ground island/cape shore -bank
farm
site
point_of_compass
edge
distance
house(body)[housing])”. However, this procedure spent
a lot of time.
To improve the problem, we have prepared a lot of“sets” for constraint description. A “set” is a group of
constraints to be applied commonly to similar caseframes. If a common “set” can be applied to the caseframes including “a place where a person can sit down”,
the “set” can be used to the opposite concepts such as“stand up” too. We give a name for showing a “set” ofnoun concepts to be used as constraints. A “set” ofconstraints with a name can be applied to various caseframes commonly as a whole. For instance, the “set” of“inhabitable location” is equal to a “set” of constraints,“(lodginghousingarea-area(scope)area(human_activity) -land -world foot_of_mountain
mountain_pass valley ground island/cape shore -bank
farm
site
point_of_compass
edge
distance
house(body)[housing])”. The set is commonly used tothe constraints in various events, such as “N2” in “N1move to/into N2”. As another example, for “N1 take out
N2 from N3”, the set of “a person of such age who cantake care by herself or himself” is applied to “N1” andthe set of “transportable object” is applied to “N2”. In
the first development stage of the verb conceptual
dictionary in INGS, we have defined the constraints for
only agent, counter-agent and, object because they areminimal information of making a sentence. And then, on
the basis, we have added the constraints for location
cases [41]. Finally, 38 sets have been defined and usedin the present version of the verb conceptual dictionary.
5.3. Defamiliarization using constraints
This part is out of the direct theme of the paper. In the
combination with the field of narrative generation, thepossibility of a variety of novel and difficult
applications emerges in the use of the conceptual
dictionaries in INGS. “Defamiliarization” using theconstraints is an application [42]. Fantastical events orphysically impossible events often appear in narratives.
Our narrative generation project or INGS aims to beable to flexibly generate from more realistic narratives
or event sequences to more fantastical ones. Realistic
narratives and fantastical narratives respectively needdifferent ranges of constraints. The proposed conceptual
dictionaries in this paper provide a foundation of thefunction of defamiliarization. The above application
adjusts the semantic constraints of concepts in a single
event in a short advertising scenario generated using apart of INGS. The function has been implementing byusing “defamiliarization” techniques. The idea wasoriginally acquired from a rhetorical analysis of actual
television commercial films. The analysis defined three
types of standard rhetoric to generate ordinary eventsand nine types of irregular rhetoric to generate
extraordinary events. We call these twelve types ofrhetoric “product introduction techniques”. In
advertising narratives, events with a product are
generated through the techniques. The standard or
ordinary
techniques
draw
theprocessof“manufacturing”, “purchasing”, and “usage” of a
product, while the extraordinary techniques conduct ona kind of deviance processing by the defamiliarization
techniques for “actor’s action”, “product’s state”,“background or place”, “actor himself (herself)”, and soon. Defamiliarization means a literary idea [43] thatchanges a familiar object into unfamiliar one toreinforce the impression. The impression of a familiar
product can be reinforced by applying of defamiliarization
techniques to the objects and agents or actors. The
technological idea will be able to generalize to one of thebasic concepts of INGS, which we have called it the conceptof “norm and deviation”, more than advertising narrative
processing. The basis of the mechanism is the conceptual
dictionaries, especially the constraints.
6. Verification and Consideration
This section tentatively verifies and considers thecurrent achievement of the conceptual dictionaries fromsome view points for the future reform and expansion.
6.1. The validity of the constraints
For the verb concepts to which a constraint “set” isapplied, we verified the validity and the classification ofnoun concepts associating to the constraints. The
criterion is whether physically possible events can begenerated under the limitation of each constraint. Such
event as “A salesgirl divorces her husband” is possible
and such event as “A spokesman is sailing on the paper”
is impossible.
We implemented an experimental event generation
program that selects a noun concept for each case in thecase frame at random according to the constraint. Using
the program, 20 events were generated by 100 caseframes selected at random from 1158 case frames inwhich all constraints were set. Three evaluators checked
the generated total 2000 events dependent on the

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standard of “possible/impossible”. We decided that theconstraints in the case frame were relatively adequate if
14/20 or more events were “physically possible”. Whenthe results were different by evaluators, we employed
the biggest number.
As a result, success (relatively adequate) was 70
and failure (inadequate) was 30. For 12 in the 30
failures, we modified them by using another set of
constraints. For example, in the case frame of “N1collect N2 from N3”, we may replace the set of“N2:object” from “goods, status and notion to be able tosend” to “goods to be able to send”. For 15 in the
failures, we modified the constraints themselves. For
instance, in the case of “N1 sail N2”, we changed theconstraint of “N2:location” to ”(river waterway lakesea)” from ”(river waterway lake sea sky)”. The latter
was based on the set, “the place to be able to pass withship and aircraft”. The other two failures were simple
description errors of case frame. Remaining one
problem was the case that the verb concept itself is
impossible such as “N1 cast a spell on N2”.
By using the above standard of evaluation, thedifference of feeling by evaluators and the ambiguity ofevaluation diminished more than previous criterion,
natural/unnatural. And it contributed to the simplicity ofsystem development. However, there is also thedifficulty that we cannot eliminate possible but
ordinarily unnatural events. Because it is hard to absorb
such kind of contextual knowledge into the conceptual
dictionaries, the solving needs to be given in the relation
with the above-mentioned defamiliarization rhetoric.
Our basic policy is that the conceptual dictionaries
provide relatively simple and standard conceptual
knowledge, while a variety of narrative knowledge suchas defamiliarization operates and adjusts more complex
and advanced literary or artistic rhetorical techniques. In
INGS, the latter knowledge can be stored and organized
into various knowledge bases including story contents
knowledge base and various techniques such as story
techniques and narrative discourse techniques utilize
and operate the knowledge to complicate and elaborate
the narrative generation.
6.2. The procedure of defamiliarization processing
This section presents a processing flow of thedefamiliarization concept applying constraints based on
the simplest way [42]. First, the user selects anadvertising product name as an object concept from a
products list prepared by the developer and the number(1-3) to specify a type of “standard techniques” thatmeans the techniques to make an ordinary event in an
advertising context. Based on the information, thesystem generates an ordinary event by acquiring a verb
concept and the noun concepts within the selected
standard technique for the product. These verb and noun
concepts are prepared for each product. Next, the user
inputs the number of an irregular (extraordinary or
defamiliarization) technique and the system rewrites theordinary event to an extraordinary event by applying thecorresponding rhetorical technique to one or more of theelements in the former event. The conceptual
dictionaries are used for applying the extraordinary
rhetoric. A defamiloarization technique replaces an
original noun concept in a target event with anothernoun concept in the different category by changing thereference space in the noun conceptual dictionary.
The mechanism, for example, generates an eventwith the product of “car” according to the“defamiliarization of location” technique. First, themechanism generates an event such as ”(drive (agent
woman) (instrument car) (location plateau))” by using
the noun concept of “car” included in the constraint.
Next, the mechanism replaces the noun concept
corresponding to the location case with another concept
outside the constraint using the defamiliarization
technique to generate a new event at a strange placesuch as “(drive (agent woman) (instrument car)(location seabed))”. The verb conceptual dictionary
principally defines a standard semantic range for eachnoun concept in an event from the viewpoint of actual
possibility or physical possibility. In contrast,
extraordinary or irregular events are constructed byseveral types of defamiliarization techniques dependent
on a kind of constraint relaxation mechanism. We haveintroduced the defamiliarization mechanism into thestory generation mechanism in INGS as a group of story
techniques. But, the result had not been efficiently
effective because of the random or non-strategic
application of the defamiliarization techniques and soon. More systematic and elaborating approach will benecessary in the future implementation.
7. Conclusions
In this paper, we have presented two dictionaries for
Integrated Narrative Generation System (INGS), verb
conceptual dictionary and noun conceptual dictionary.
Each of the dictionaries has a common conceptual
hierarchy of single inheritance by is-a relations. Eachterminal concept in the verb conceptual dictionary
describes one or more case frame(s), the main element’s
constraints, and a sentence pattern. The basic
description of each terminal concept in the noun
conceptual dictionary associates the concept to the othernoun concept. Although a noun concept needs an

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attribute frame, the description is incomplete in thepresent version. The purpose of this research is notdeveloping general conceptual dictionaries, but building
them as a mechanism in narrative generation. In
particular, we have actually and organically combined
the two conceptual dictionaries with INGS. The specificform of an event that is a basic unit in a narrative is
given by a case frame and the constraints in the verb
conceptual dictionary, and the content knowledge isprovided according to the noun conceptual dictionaries.
We have presented the basic mechanism based on thecooperation of two types of conceptual dictionaries in
INGS. The research has various problems and futureissues. One issue to be considered is about the validityof constraints. Since narratives have a width fromrealistic style to fantastical style, plural standards should
be dealt with in a narrative generation mechanism. We
have presented an idea by adjusting and operating
constraints using the rhetoric of defamiliarization. As
the other future issues, we have referred to expanding
the hierarchy of the verb conceptual dictionary anddeveloping the parts of proper nouns and attribute
frames in the noun conceptual dictionary.
References
1. R. C. Schank and R.P. Abelson, Scripts, Plans, Goals,
and Understanding: An Inquiry into Human Knowledge
Structures (Lawrence Erlbaum, NJ, 1977).
2. J. R. Meehan, The Metanovel: Writing Stories by
Computer (Garland Publishing, NY, 1980).
3. E. T. Mueller, Daydreaming in Humans and Machines(Ablex, NJ, 1990).
4. S. R. Turner, The Creative Process: A Computer Modelof Storytelling and Creativity (Lawrence Erlbaum, NJ,
1994).
5. N. Okada and T. Endo, Story generation based on
dynamics of the mind, Computational Intelligence. 8(1)(1992) 123-160.
6. L. Pemberton, A modular approach to story generation, inProceedings of the Fourth Conference on European
Chapter of the Association for Computational Linguistics(1989) 217-224.
7. R. R. Lang, A declarative model for simple narratives, inNarrative Intelligence: Papers from the 1999 AAAI Fall
Symposium, Technical Report FS-99-01 (1999) 134-141.
8. S. Bringsjord and D. A. Ferrucci, Artificial Intelligenceand Literary Creativity: Inside the Mind of BRUTUS, a
Storytelling Machine (Lawrence Erlbaum, NJ, 1999).
9. J. Bates, Virtual reality, art, and entertainment, The
Journal of Teleoperators and Virtual Environment. 1(1)(1991) 133-138.
10. N. Montfort, Generating Narrative Variation inInteractive Fiction (A Dissertation in Computer andInformation Science, University of Pennsylvania, 2007).

11. T. Ogata, and A. Kanai, An Introduction to Informatics ofNarratology: Towards the Thoughts and Technologies ofNarrative Generation (Gakubun-sha, Tokyo, 2010).
12. T. Akimoto and T. Ogata, (2012) Macro structure andbasic methods in the integrated narrative generation
system by introducing narratological knowledge, inProceedings of 11th IEEE International Conference on
Cognitive Informatics and Cognitive Computing (2012)
253-262.
13. T. Akimoto and T. Ogata, (2014). An information designof narratology: the use of three literary theories in anarrative generation system, The International Journal of
Visual Design (2014) in press.
14. T.
Ogata,
Expanded
literary
theory:cognitive/computational expansion of literary theories
and narratology, in Proceedings of 17th Congress of theInternational Association of Empirical Aesthetics (2002)
163-166.
15. T. Ogata, S. Imabuchi and T. Akimoto, Narratology andnarrative generation: expanded literary theory and theintegration as a narrative generation system (2), in
Proceedings of the 5th Augmented Human International
Conference (2014) P-10.
16. T. Akimoto and T. Ogata, The expansion of paths in themutual transformation mechanism of music and narrative,
International Journal of Cognitive Informatics andNatural Intelligence. 7(4) (2013) 44-63.
17. T. Akimoto and T. Ogata, Circulative narrative
generation based on the mutual transformation between
narrative conceptual structures and music in the
integrated narrative generation system, in Proceedings ofInternational Conference on Artificial Life and Robotics(2014) 36-39.
18. K. Oishi and T. Ogata, Towards the development ofconceptual dictionary for narrative generation system, in
7th International Conference on Natural Language
Processing and Knowledge Engineering (2011) 351-358.
19. K. Oishi and T. Ogata, The development of conceptual
dictionary for narrative generation system: The structureand functions. in Proceedings of the 4th IEEE
International Conference on Digital Game andIntelligent Toy Enhanced Learning (2012) 168-170.
20. K. Oishi, Y. Kurisawa, M. Kamada, I. Fukuda, T.
Akimoto and T. Ogata, Building conceptual dictionary
for providing common knowledge in the integrated
narrative generation system. in Proceedings of the 34th
Annual Conference of the Cognitive Science Society(2012) 2126-2131.
21. N. McIntyre and M. Lapata, Learning to tell tales: a datadriven approach to story generation, in Proceedings ofthe 47th Annual Meeting of the ACL and the 4th IJCNLP
of the AFNLP (2009) 217-225.
22. S. Ikehara, M. Miyazaki, S. Shirai, A. Yokoo, H.
Nakaiwa, K. Ogura, Y. Ooyama and Y. Hayashi, GoiTaikei: A Japanese Lexicon, CDROM (Iwanami-Shoten,
Tokyo, 1999).

Published by Atlantis Press
Copyright: the authors283

23. F. Bond, H. Isahara, S. Fujita, K. Uchimoto, T.
Kuribayashi and K. Kanzaki, Enhancing the Japanese
WordNet, in The 7th Workshop on Asian Language
Resources, ACL-IJCNLP 2009 (2009) 1-8.
24. EDR Electronic Dictionary. http://www2.nict.outpromotion/techtransfer/EDR/J_index.html
25. Japanese WordNet. http://nopwww.nict.go.jp/wn-ja
26. K. Onodera and T. Ogata, Sequence generation based on
mutual relationship between state and action: as amechanism in narrative generation system, in
Proceedings of the 4th DIGITEL (2012) 159-161.
27. V. Y. Propp, Morphology of the Folktale, 2nd Ed. Transl.
L. Scott. (TX: University of Texas Press, TX, 1968)
28. S. Imabuchi and T. Ogata, A story generation system
based on Propp theory: as a mechanism in an integrated
narrative generation system, in Lecture Notes in Artificial
Intelligence 7614, eds. H. Isahara and K. Kanzaki 312321.
29. S. Imabuchi and T. Ogata, Methods for generalizing thePropp-based story generation mechanism, in Lecture
Notes in Computer Science 8210, eds. T. Yoshida, G.
Kou, A. Skowron, J. Cao, H. Hacid and N. Zhong(Springer-Verlag, NY, 2013) 333-344.
30. T. Akimoto and T. Ogata, Towards a discourse
mechanism in narrative generation system: proposal of asystem introducing narrative discourse theory andreception theory. Cognitive Studies. 20(4) (2013) 396420.
31. G. Genette, Discours du Récit, Essai de Méthode,Figures III. (Seuil, Paris, 1972). Transl. J. E. Lewin,
Narrative Discourse: An Essay in Method. (Cornell
University Press, NY, 1980).
32. H. Sato, K. Kobayashi, T. Yasuda and T. Ogata, A Simple
Generation of Single Sentences and Complex
Sentences in a Narrative Generation System, in
Proceedings of the Japanese Information Processing
Society Annual Conference (4) (2008) 4ZH-6 (4～803804).
33. T. Ogata, Towards image generation in a narrative
generation system, in Theories and Practices of Image
Editing, eds. A. Kanai and Y. Niwa (Hosei University
Press, Tokyo, 2008) 165-235.
34. H. R. Jauss, Literaturgeschichte Als Provokation.
(Suhrkamp Verlag, Frankfurt am Main, 1970). Transl. T.
Bahti, Literary history as a challenge to literary theory, in
Toward an Aesthetic of Reception (University of
Minnesota Press, MN, 1982) 3-45.
35. N. Okada, Representation and Storage of Word Concepts(The Institute of Electronics, Information andCommunication Engineers, Tokyo, 1991).
36. K. Takeuchi, Construction of thesaurus of predicateargument structure for Japanese verbs. in Proceedings ofthe 25th JSAI (2011) 3H2-OS3-5.
37. C. J. Fillmore and C. Baker, A frames approach tosemantic analysis, in The Oxford Handbook of Linguistic
Analysis, eds. B. Heine and H. Narrog (Oxford
University Press, 2010) 313-339.

38. K. H. Ohara, Lexicon, grammar, and multilinguality in
the Japanese FrameNet, in Proceedings of the 6thInternational Conference on Language Resources and
Evaluation (2008) 3264-3268.
39. K. Kipper-Schuler, G. T. Dang and M. Palmer, Classbased construction of a verb lexicon, in Proceedings of
AAAI-2000 (2000) 691-696.
40. K. Kipper-Schuler, VerbNet: A Broad-Coverage,
Comprehensive Verb Lexicon (Ph.D. thesis, Computerand Information Science Department., University ofPennsylvania, 2005).
41. K. Sugita, T. Akimoto, J. Ono and T. Ogata, The
constraint definition of location cases to the verb
conceptual dictionary in an integrated narrative
generation system, in Proceedings of the Japanese
Language Processing Society Annual Conference (2014)
221-223.
42. Y. Zhang, J. Ono and T. Ogata, Single event and scenario
generation based on advertising rhetorical techniques
using the conceptual dictionary in narrative generation
system, in Proceedings of the 4th DIGITEL (2012) 162164.
43. V. Shklovsky, Transl. B. Sher, Theory of Prose (Dalkey
Archive Press, IL, 1990).

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