Journal of Robotics, Networking and Artificial Life, Vol. 3, No. 3 (December 2016), 209-212

A Way for Using the Verb Conceptual Dictionary in an Integrated Narrative Generation
System: the Use of Co-occurrence Information on Verb Concepts
Takashi Ogata

Faculty of Software and Information Science, Iwate Prefectural University152-52, Sugo Takizawa, Iwate 020-0693, Japan
Jumpei Ono

Graduate School of Software and Information Science, Iwate Prefectural University152-52, Sugo Takizawa, Iwate 020-0693, Japan
E-mail: t-ogata@iwate-pu.ac.jp, g236m001@s.iwate-pu.ac.jp

Abstract
This study presents a method of selecting verb concepts based on analysis of co-occurrence information of verbs inmodern Japanese novels collected from the Japanese digital library, “Aozora Bunko”. We plan to incorporate theproposed method into an integrated narrative generation system (INGS) that synthesizes various mechanisms (story
generation, sentence generation, and so on) for narrative generation. We are currently developing such a system. In
this study, we provide an overview of INGS and examine mechanisms related to the proposed method. In a verb
conceptual dictionary that we have developed, as verb concepts of a variety of difficulty (comprehensibility) aremixed, the objective in this study is to adjust difficulty of verb concepts by using co-occurrence information onverb concepts. Our hypothesis is that the original concepts of co-occurrence and the resulting concepts have aproportional relationship. We investigate this hypothesis by incorporating proposed mechanism into an INGS.
Keywords: Co-occurrence information, Integrated narrative generation system, Verb conceptual dictionary, Verb
concept

1. Introduction
This paper mainly focuses on an event generation in thestory generation mechanism and verb conceptual
dictionary in an integrated narrative generation system(INGS)1-3 (these details are mentioned in the section 2).
An event composed of a verb concept and noun
concepts. However, their concepts are selected
randomly in an event generation in the INGS.
Therefore, we previously proposed a method of the verb
concept selection based on frequency information4.
Here, we propose a method of the verb concept
selection based on co-occurrence information.

2. The Architecture of an Integrated Narrative
Generation System
The architecture of our INGS is shown in Fig. 1. The
system is composed of a concept generation mechanism
used to generate a story structure and edit it (discourse),
and a surface representation mechanism to represent anarrative. These mechanisms use dictionaries (storedconcepts5, language notations6), story contents
knowledge base (stored knowledge for generating
structure of narrative)7, the state-event translation
knowledge base (stored knowledge for connecting
between each event and states in the conceptualstructure)3.

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209Takashi Ogata, Jumpei Ono
Conceptual expression
Input：
parameter

Story

Discourse

Surface expression

Sentence

MusicMovie

4. Acquiring Co-occurrence Information

Knowledge
Output：narrativeDictionary(concept,notation)

State-event
translation
knowledge basestory techniques and story contents knowledge. The
objective of this paper is to revise this selecting method.

Story contents
knowledge baseFig. 1. Architecture of INGS

3. Overview of Conceptual Dictionaries
Conceptual dictionaries have a hierarchical structurefrom higher to lower concepts.
A) Hierarchy of Noun Concepts
The noun conceptual dictionary currently contains
115,765 terminal concepts and 5,809 intermediateconcepts. Each intermediate concept has a list ofhyponymy terminal concepts, the number of depths in
the hierarchy, the super-ordinate concepts, and a list of
the hyponymy intermediate concepts.
B) Hierarchy of Verb Concepts
The verb conceptual dictionary has 11,951 terminal
concepts and 36 intermediate categories. Fig. 2. shows
the description of the verb concept “bite.” Each terminalverb concept has the following three elements: 1) a
sentence that includes the verb (“sentence-pattern” in
Fig.2.), 2) the types of required noun cases (“caseframe”), and 3) defining the range which each noun
concept in the case-frame(s) requires (“constraint”).
((name 齧る 2 [bite])(sentence-pattern“N1 が N2 を齧る”[N1 bite N2])
(case-cons-set((case-frame((agent N1) (object N2) …))(constraint((人[human] -死人[corpse] -人間〈人称〉[human<person>]-準人間[semi-human])(食料[food] -調味料[flavoring] -飲物・たばこ[drink・cigarette] –汁[soup])))…) …)
Fig. 2. Description of the verb concept “bite”C) Generating an Event Concept in the Story
Generation Mechanism
A story generation process expands a story structure byusing story techniques. This process includes an eventgeneration. An event is generated based on thedescription of the case-frame. A verb concept that isused in an event concept generation is decided based onA) Used Texts and Method
We used “Aozora Bunk”, which includes 4980 texts ofprimarily Japanese novels published from 1872 to 1963,
to acquire co-occurrence data of verbs. At first, cooccurrence information of all verbs in the texts wasacquired.
The following (1) is a formula in KH Coder8 tocalculate the co-occurrence data. In the formula, Sa isequal to the total number of sentences that include verba in the text; Sb is the total number of sentences thatinclude verb b in the text; Sa ∩ Sb is the total number ofsentences including both a and b in the text; and Sall is
the total number of sentences included in the text.

|𝑆𝑎 ∩𝑆𝑏 | |S𝑏 |𝑓=

�|𝑆𝑎 |�

�S𝑎𝑎𝑎

(1)

B) Result
The co-occurrence information was corresponded to4866 terminal verb concepts in the verb conceptual
dictionary in INGS. As an example, Table. 1 shows cooccurrence data of “bite”. Each verb concept connects
to 143.44 verb concepts in average.
Table. 1. Co-occurrence data of “bite”

Co-occurrence
information

141.1954
70.5977
1.035663
1.001386

Relating verb concepts
打 ち 上 げ る 1[shoot up], 慕 う
1[pout], etc.
飽 き る 1[loss intent], 謀 る
1[deceive], etc.
…<Omitted>…
出来る 7[be able to]
戻る 1[return]

5. Preliminary Questionable Research
We evaluated the effectiveness in the verb concepts
selection based on the acquired co-occurrence
information.
5.1 Preparation
Four lists of verb concepts, which were extracted fromthe stories generated by using a partial mechanism inINGS, were used in the questionable research.

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A Way for Using

A) A Used Story Generation Method in INGS
The story structures were generated by using “storycontents grammar”9 and the mechanism that we havebeen developing for a part in INGS. A hierarchy of thestory contents grammar has a layer of “functions”. Thelevel is the most fundamental and a sequence offunctions form a basic structure of a story. And, eachfunction is extended to the lower level where one ormore verb concepts are instantiated by selected from thecandidates.
Fig. 3. shows used functions and the fixedsequence. Each of the functions is rewritten by thelower levels to reach to the level of one or more
concrete verb concepts.
<Absentation>→<Interdiction>→<Violation>→<Villainy>→<Mediation>→<Beginning counteraction> → <Departure> → <First “Function” ofdonor> → <Hero's reaction> → <Receipt of amagical agent> → <Guidance> → <Struggle> →<Blanding> → <Victory> → <Liquidation> →<Return>→<Unrecognized arrival>→<Unfoundedclaims> → <Recognition> → <Exposure> →<Transfiguration>→<Punishment>→<Wedding>Fig. 3. A used sequence of functionsB) Making Verb Concept Lists
We acquired co-occurrence data using the above story
generation mechanism by the following method. Whenthe mechanism selects a concrete verb concept fromgenerally many verb concepts, it utilizes co-occurrence
data as shown in the next types.
[Max] The story generation mechanism generated 5stories by repeatedly using a verb concept that has thestrongest co-occurrence with the prior verb concept andfinally decided 39 verb concepts in one story selected
randomly from the above stories.
[Middle] The mechanism generated 5 stories by
repeatedly using a verb concept that has the medium cooccurrence with the prior verb concept and finallydecided 41 verb concepts in one story selected randomly
from the above stories.
[Min] The mechanism generated 5 stories by repeatedlyusing a verb concept that has the weakest co-occurrencewith the prior verb concept and finally decided 47 verb
concepts in one story selected randomly from the abovestories.

[Random] The mechanism generated 5 stories byrepeatedly using a verb concept randomly for the priorverb concepts and finally decided 41 verb concepts in
one story selected randomly from the above stories.
In addition, in the following cases, a verb concept
is selected randomly: 1) the first verb concept in story
generation, 2) when two or more candidates exist in theselection timing, 3) when no candidates with cooccurrence data in the selection timing.
5.2 First EvaluationA) Goal and Process
The goal is to check the relationship between the degreeof co-occurrence information and the degree ofcomprehensibility by the users. The subjects were fivestudents. They read four kinds of verb concepts lists(max, middle, min, and random) to select an value from
1 (easy) to 5(difficult). We set a time limit of fiveminutes for each verb list.
B) Result
As shown in Table. 2 shows the result, we could not see
clear differences among the subjects. In short, cooccurrence information of verb concepts does not
influence the comprehensibility.
Table. 2. Evaluation result[Max]ABCDE
1.23 2.54 2.87 3.08 2.82

Average
2.51[Middle] 1.29 2.73 3.00 2.80 3.10
1.26 2.50 3.02 2.85 2.65
[Min]

2.59
2.46

[Random] 1.15 2.60 2.93 2.80 2.63

2.42This result was similar to the case of noun concept
selection based on co-occurrence information that we
previously implemented10. Also, frequency and cooccurrence are clearly different methods to adjust verb
concepts selection in INGS.
5.3 Second EvaluationA) GoalOn the other hand, in the noun concepts selection using
co-occurrence information, we confirmed anothermethod. In the method, a noun concept is associatedwith other noun concepts that have the similar degree of

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Takashi Ogata, Jumpei Ono

co-occurrence information11. We adapt this method toverb concepts selection.
B) Process
For the above stories of max, middle, and min, wecalculated the absolute values of difference (D) between
the value of a verb concept X and the value of the next
verb concept Y. Fig. 4 shows the procedure. As the caseof noun concepts selection, we consider if D is lower, itindicates the similar comprehensibility. However, it isnecessary to be actually investigated the consideration.
In this paper, we check whether lower D is resulted byhigher degree of co-occurrence. It enables to adjust
selecting verb concepts using co-occurrence of verb
concepts.

Evaluation ofverb X : 1.0
Evaluation ofverb Y : 2.0

D(X,Y)=| 1.0-2.0 |= 1.0

Fig. 4 Calculating DC) Result
Table 3 shows the result of calculating D. In this Table3, we did not use verb concepts that were selectedrandomly. The average of D in [Max] was smaller thanothers. If lower D is related to the similar degree ofcomprehensibility of the verb concepts, we may be able
to use this result to adjust the degree ofcomprehensibility of verb concepts.

Method[Max]

Table 3. The result of calculating D
The number of theAverage of D
evaluated verb concepts
120.43[Middle]150.76[Min]

140.96

6. Conclusion
This paper preliminarily proposed a method of a verb
concept selection by using co-occurrence information
for the conceptual event generation mechanism in INGS.
The results indicate a next design direction of the verb
concepts generation in INGS: to use verb concepts thathave co-occurrence relationship for prior verb concept
enables to select them by similar degree ofcomprehensibility. The proposed method may useful toadjust the verb concepts selection in INGS.

In the future, we plan to precisely improve themethod to finally integrate methods of concepts
selection in event generation in INGS using frequency
and co-occurrence of words.
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