論

日本大学生産工学部研究報告B
2007 年 ６ 月 第 40 巻

文

単語分布と専門語彙の関連付けに関する研究

内山将夫 ・中條清美

Linking Word Distribution to Technical Vocabulary

Masao UTIYAMA and Kiyomi CHUJO

We describe a type of plot called wordplot that is designed to visualize the distribution of words in a
specific domain in comparison with their distribution in general English. The wordplot is intended togive English teachers and developers of pedagogical materials a convenient way of selecting technicalvocabulary. Words in certain regions of these wordplots occur frequently in specific domains andinfrequently in general English. A detailed examination showed that the words in these regions were
technical vocabulary,demonstrating the effectiveness of the wordplots in identifying technical vocabulary.
Keywords: Word Distribution,Technical Vocabulary,Scatter Plot,Visualization,Statistical Measures
The list consists of 2971 individual words with their

1. Introduction

frequencies in the Commerce domain. It was createdfrom the Commerce component of the BNC as de-

We describe a type of scatterplot that is designed toscribed in Section 3. In Fig.1, this list is comparedvisualize the distribution of words in a specific domain

with the BNC High‑Frequency Word List (BNC

in comparison with their distribution in general Eng-

HFWL), a list of 13956 individual words with theirlish. We use the British National Corpus (BNC) tofrequency in the whole BNC (Chujo, 2004). Because

represent general English and the Commerce andeach word in the Commerce word list is also included

Natural Science domains,which are part of the BNC,

in the BNC HFWL,we can compare the frequency of

to show the effectiveness of the plot in identifying

each word in the Commerce word list with that of thetechnical vocabulary in these domains. The plot issame word in the BNC HFWL as described in Table 1.

intended to give English teachers and developers of

The sizes of these word lists are shown in Table 2.

pedagogical materials a convenient way of selecting
technical vocabulary.

In Fig.1, the horizontal axes F

and P

represent the frequency and proportion of the fre-

We call this particular type of scatterplot wordplot.
A wordplot of a Commerce word list is shown in Fig.1.

quency of each word in the BNC HFWL.The verticalaxes F

情報通信研究機構主任研究員
日本大学生産工学部教養・基礎科学系助教授― 13 ―

and P

represent the frequency andTable 2

Size of word lists

Word list

No. of types

No. of tokens

Commerce

2971

5877355

Natural Science

2323

805592

BNC HFWL

13956

86008037“No.of types”is the number of individual words in each word
list.
“No.of tokens”is the total number of running words in eachword list, which is obtained by summing the frequencies of allwords in each list. The procedure used to create these lists is
described in Section 3.

Table 3 Number of individual words in eachregion for the Commerce word list

Fig.1 Wordplot of Commerce word list
proportion of the frequency of each word in theCommerce word list and dots represent words in theCommerce word list.

LH＝29

MH＝191

HH＝771991

LM ＝320
LL＝641

MM ＝506
ML＝293

HM ＝164
HL＝56

990

990

990

991

2971

Fig.1 is divided into nine regions: HH, HM, HL,

990

M H,M M ,M L,LH,LM ,and LL. X (and Y)of region
XY means that the frequencies of words in that region

BNC and in the Commerce domain are correlated.

are High (H), M edium (M), or Low (L) in the BNC

High‑frequency words in the whole BNC tend to

HFWL (and Commerce word list). To set the bound-occur frequently in the Commerce domain. This ten-aries for the X‑regions (Y‑regions), the words in thedencyis also shown in Table 3.However,it can also be

BNC HFWL (Commerce word list) were sorted inseen that some words occur more or less frequently indescending order of their frequency and divided into

the Commerce domain than in the BNC.

three sublists of the same length. The boundary

A notable feature of Fig.1 is that words distributed>P

between H and M was set at the highest frequency

in the upper region of the diagonal line(Pword in the medium‑frequency word list and that

occur relatively more frequently in the Commerce

between M and L was set at the highest frequency

domain.This observation is important because techni-

word in the low‑frequency word list. The number ofcal words tend to occur relatively frequently in a

individual words in each region is shown in Table 3.

specific domain in comparison with in general English

The marginal frequencies are in the last column and(Nation,2001). Thus,it is likelythat technicalvocabu-

last row.

lary will be found in the upper region of this diagonal

A diagonal line, P＝P, appears in Fig.1.

)

line.

Words (dots)are distributed along this line,indicating

Based on this observation, we conjectured thatthat the frequency distribution of words in the wholewords in the LH and LM regions were likely to be

Table 1technical words in the Commerce domain because

Frequency pattern of word

words in these regions occur relatively frequently in
SP

BNC

that domain. To examine this idea, the highest F
word in each region was displayed in Fig.1 to see
which words were distributed in the region. The

notThis table indicates the pattern of the frequency of wordin the Commerce or Natural Science(SP)word lists in comparison with the frequency of the same word in the BNC HFWL :and stand for the frequencies of word in the SP word list
and the BNC HFWL, respectively, stands for the number ofrunning words in SP not involving word ,and stands for thenumber of running words in BNC HFWL not involving word .
＋ and ＋ are the numbers of total running words in theselists, whose actual values are listed in the column “No. of
tokens” in Table 2.We use
,
,
and
to
denote , , ＋ , and ＋ , respectively.

highest F

10 words in each region are listed inTable 4. In Table 4, an ＊ denotes words that are notincluded in the JSH,a word list compiled from juniorand senior high school English textbooks in Japan,asdescribed in Section 2. The results generally supported our conjecture (the table is discussed in detail in
Section 3). In the Appendix, we include App.Fig.1
and App.Tables 1 and 2 to show that our observations― 14 ―

of the Commerce word list can also be applied to the

2. Background

Natural Science word list.
Below we describe the background to the development of wordplots and the procedure used to create

To become proficient in English, learners mustthe word lists we used in this work.We then examine

expand their vocabulary (Nation, 2001). Consequent-

the nature of the words in each region to evaluate thely,various word lists have been developed.These lists

usefulness of wordplots as a means of identifying

can be broadly categorized into three types, general,technical vocabulary.

academic,and technical word lists,depending on their

Table 4 Highest ranked 10 words in each region for
the Commerce word list

purposes.
General words Lists of general words are intended
to provide basic English words. An example is A

LH

MH

HH

dividend

employeetheIt includes the 2000 most basic words from the Interim

sellerasset

beReport on Vocabulary Selection (Faucett et al., 1936)

audit

creditofthat are considered necessary for learning English as

equitystock

toa foreign language.The criteria for vocabulary selec-

suppliercashadiscount

loan

and

auditorconsumerinaccountant

investorthatmerger

buyer

have

monetary

debt

it

LM

MM

HM

from the top‑selling series of junior and senior highpremiumprospectprime

school textbooks in Japan (the New Horizon 1, 2, 3

aggregateordinary

image

series and the Unicorn I, II and Reading series).

lease

external

population

Japanese high school students generally use these or

stake

examination

easilysimilar books to study English before entering univer-organizational

adviser

context

sity.

innovation

statutorypound

Academic words Academic word lists highlightmonopoly

consultant

nearwords that university students are likely to meet in a

incentive

practical

versioncompletion

importpicturepublisherquation

bear

LL

ML

HL

seminarmere

animalauctiontradition

surface

570 words selected according to range and frequency

complexity

skin

son

criteria and incorporates words not included in theconsensus

pool

edge

GSL.M ore than 94% of the words in the list occur in

respondent

constitutehusband20 or more of the 28 subject areas of the corpus.

unchanged

pure

baby

Technical words Unlike general and academic word

redemption

povertymediumlists that cover a wide range of general and academic

goodwill

healthyfatherEnglish, technical vocabulary is intended to coverreasonablenessbeginning

maybe

technical words that students meet in specific fields.

strictly

distancemarry

General Service List of English Words (West, 1953).

tion include word frequency, ease or difficulty of learning, necessity, cover, stylistic level, and intensive andemotional words. We call this list the GSL in thispaper.
We also created a general word list consisting of
3098 words, which we call the JSH. It was compiled

wide range of academic texts. The ʻ
Academic Word
List (AWL)ʼ(Coxhead, 2000) for written academic
English was compiled from a 3.5 million‑word＋ corpus of academic texts covering subject areas such as
the arts,commerce,law,and science.The list contains

Thus, English teachers must provide technical words
that will satisfy their studentsʼneed. This is a challenging task when using the traditional vocabulary
selection criteria of frequency and range (Sutarsyah etal.,1994). Because the focus of these two measures is― 15 ―

ranking general‑purpose vocabulary in order of prior-payout, issuer, liquidity, salesperson, settlor,ity, separating technical vocabulary from generalacquirer, volatility, accountancy, lender,‑purpose vocabulary is still labor‑intensive,time‑con-depreciation, tax‑free, investor, dividend,suming,and heavily dependent on the selectorʼ
s exper-

relocationtise in English education and specialist knowledge of

These lists indeed contain different‑level words,

the domain. English teachers generally do not have

which is demonstrated by comparing them with otherthis specialist knowledge so there is clearly a need forreference word lists such as the JSH.

easy‑to‑use tools for identifying technical vocabulary.

However, we noticed a gap in the difficulty levels

A number of corpus‑based studies have used certain

between the word lists extracted by Fisher and PM I.

statistical measures to identify technical vocabulary.

Because the words identified by PMI seem to be much

For example, Nelson (2000) used the log‑likelihood

more difficult than those identified by Fisher, weratio (LLR) statistic (Dunning, 1993) to find words

believe that some words are not identified by thesethat are statistically more frequently used in businessmeasures.

English than in general English by comparing theThis concern is illustrated in Fig.2. We classified

frequency with which each word occurred in a busi-

the 2971 words in Fig.1 into four classes(Top, Middle,

ness English corpus with its frequency in the BNC.He

Bottom, and Others) according to the ranking

was able to generate a list of business‑related words

identified by Freq,Fisher and PMI.We first identified

such as, business, market, customer, management,

1741 words that occurred statistically more signifi-

price, and bank.

cantly (at the 5% level) in the Commerce word list,

Recently, Chujo and Utiyama (2005)

compared

based on Fisherʼs exact test. We then extracted top

nine statistical measures for identifying technical1740 (＝580×3) words in each measure and divided

vocabularyin the Applied Science domain in the BNC.

them into Top‑,Middle‑,and Bottom‑ 580 words.We

These measures included the frequency of each wordclassified the remaining 1231 words as Others. Thosein the domain (Freq), LLR, and pointwise mutualclassified words are shown in Figs.2a, 2b, and 2c for

information (PMI)(Church and Hanks, 1989).

Freq, Fisher and PMI, respectively.

Theyfound that each statistical measure extracted a word

Fig.2 shows that each measure extracted words

list suitable for different‑level learners of scientificfrom different regions of the wordplot.Fig.2d shows

English, i.e., LLR and PM I extracted intermediate

the words that were included in the Top‑580 words ofand advanced level technical words,respectively,and

some measures.

Freq extracted general words not specific to thatof the wordplot were not covered by these measures.

domain.

The same phenomenon was also observed in the caseWe also applied the nine statistical measures and

It clearly shows that some regionsof the Natural Science word list.

several other measures to the Commerce and Natural

It might be possible to use a statistical measure to

Science word lists and confirmed these results.Below

extract words from the regions that were not covered.

we list the top 25 words in the Commerce word list

However, we opted to examine the nature of theidentified by Freq, Fisherʼs exact test (Fisher) andwords in each region of the wordplots before devising

PMI.

any statistical measures.

Freq : the,be,of,to,a,and,in,that,have,it,

Our examination showed that we could characterizefor, they, on, will, this, by, with, as, not, or,each region in the wordplots in terms of general,

you, which, he, from, atacademic, and technical vocabulary, as described in

Fisher : market, company, bank, the, busi-

the next section. This finding suggested the idea ofness, price, rate, cost, firm, tax, investment,using wordplots as a tool for identifying technicalaccount, share, profit, contract, of, income,vocabulary.In addition,wordplots could be combined

financial, customer, management, product,with existing statistical measures to guide users toasset, will, trade, beinteresting top‑ranked regions or words identified by

PM I : lading, buyout, long‑run, arbitrage,these measures, as shown in Fig.2. (We are now

subcontractor, stockmarket, offeror, drafter,

developing a program that allows users to examine

no‑arbitrage, shareholding, headhunter,

wordplots interactively.)― 16 ―

Fig.2 Distributions of measures for Commerce word list
The Natural Science word list and BNC HFWL

3. Observations

were created using the same procedure as for theCommerce word list. The procedure used to compile

3.1 Data

the BNC HFWL is detailed in (Chujo, 2004).

We used the BNC HFWL to represent general

We used the GSL,JSH and AWL as reference lists

English and the Commerce and Natural Science word

to see which words were distributed in each region oflists to demonstrate how wordplots can identify tech-

the wordplots of the Commerce and Natural Science

nical vocabulary in these domains. These lists were

word lists.As described in the previous section,thesecreated as follows.

lists contain general and academic words. We also

We first lemmatized the Commerce component ofused the function word list (FWL)from (Nation,2001,

which

pp.430‑431) as a reference word list. The numbers

provides possible base forms and part‑of‑speechof individual words in these reference lists that are

information for each word. This created a list ofcontained in the BNC HFWL,Commerce and Natural

154669 individual lemmatized words. (For example,

Science word lists are shown in Table 5.

the BNC by using the CLAWS7 program,finances, financing, and financed would be listed as
finance. ) Then, words that occurred fewer than 100
times in the Commerce domain were deleted.Next,all

Table 5 Number of individual words contained inword lists
Word list

FWL

GSL

JSH

AWL

part‑of‑speech tags and deleted manually because

Commerce

152

1110

1548

409

“
they are of high frequency in particular texts but not

Natural Science

142

934

1261

309

in others ... and they could not sensibly pre‑taught

BNC HFWL

164

1855

2889

560

proper nouns and numerals were identified by their

because their use in the text reveals their meaning”(Nation,2001,pp.19 ‑20) .Finally,this process yielded

3.2 Comparison with the reference word lists

a 2971 word list, which we call the Commerce wordlist.

In this section, we compare the nine word lists
extracted from the nine regions (HH, HM ... LL) of― 17 ―

the Commerce wordplot in Fig.1 with the reference

Table 8Relative recall for Commerce word list

word lists. The statistics pertaining to the Natural

Region

FWL

GSL

JSH

AWL

HH

322.0

190.9

170.5

89.5

HM

83.4

197.5

172.0

88.6

HL

69.8

243.8

188.5

13.0

MH

10.2

61.7

72.3

194.0

MM

30.9

78.8

101.7

162.2

ML

46.7

127.0

116.6

91.7

LH

0.0

18.5

6.6

0.0

LM

0.0

10.0

28.2

84.0

LL

0.0

17.5

28.4

62.3

Science word list are in the Appendix.These statistics
show similar patterns to those in the Commerce wordlist.
We defined three indicators, percent inclusion at
rank 50 (PI＠50), percent inclusion (PI) and relative
recall (RR)to compare each word list with the reference word lists.
Let TOP and ALL be the top 50 words and all words
in the word list extracted from a region.These words
are sorted in descending order of their frequency in
the Commerce word list.Let REF be the all words in
TOP REF ×a reference list. Then, PI＠50＝
100,
TOP
ALL
REF
×100. PI＠50 and PI are used to
PI＝ALL
show what percentages of the words in the top‑50 andwords in the reference list compared with the expected recall.
PI＠50,PI,and RR for the Commerce word list are

all words in each region are contained in the reference

shown in Tables 6, 7 and 8, respectively. From these＝list. To define RR, we first define recall
ALL REF.Because the size of ALL varies accordREF
ing to each region (see Table 3), we divideby theALL
expected recall ＝
to calculate RR＝ ×100.
2971＞＝
When RR
100,then the region contains manymore

tables,it is clear that words in the FWL occur mainlyin the HH region. This is expected because function
words occur frequently regardless of domains. Next,
general words represented by the GSL and JSH occur
mainly in HH, HM, and HL. That is, words in theseregions are regarded as general words.Finally,words

Table 6 Percent inclusion at rank 50 for Commerceword list
AWL

in the AWL often occur in MH and M M, whichsuggests that words in these regions are academic

Region

FWL

GSL

JSH

HH

82.0

100.0

100.0

0.0

HM

8.0

70.0

90.0

14.0

The tables clearly show that words in LH,LM,and

HL

2.0

92.0

98.0

2.0

LL are not covered by the reference word lists. That

MH

0.0

14.0

32.0

38.0

is, words in these regions are neither general nor

MM

0.0

28.0

46.0

22.0

academic words. We will now examine the words

ML

4.0

46.0

56.0

18.0

contained in these and other regions by looking at the

LH

0.0

6.9

3.4

0.0

top‑50 words in each region. (In future work,we will

LM

0.0

2.0

8.0

8.0

compare the words in these regions with references

LL

0.0

8.0

14.0

12.0

such as dictionaries of business English.)words.Words in ML seem to be a mixture of generaland academic words.

3.3 Qualitative evaluation
Table 7

The top‑10 words from each of the nine regions are

Percent inclusion for Commerce word list

Region

FWL

GSL

JSH

AWL

HH

16.5

71.3

88.8

12.3

HM

4.3

73.8

89.6

12.2

HL

3.6

91.1

98.2

1.8

MH

0.5

23.0

37.7

26.7

MM

1.6

29.4

53.0

22.3

ML

2.4

47.4

60.8

12.6

LH

0.0

6.9

3.4

0.0

LM

0.0

3.8

14.7

11.6

LL

0.0

6.6

14.8

8.6

shown in Table 4 for the Commerce word list and in
App.Table 1 for the Natural Science word list. Since
Table 4 and App.Table 1 show similar tendencies inthe extraction of each word lists,we will focus on theword lists in the Commerce domain. Note that theCommerce domain includes texts from related fields
such as accounting, advertising, banking, public relations, trading, and sales.
When we look at the word lists in Table 4 and App.
Table 1, the vocabulary seems to fall into three categories: (1) general vocabulary, (2) academic vocabu― 18 ―lary, and (3) technical vocabulary. These categories

ness focus. They include “business people”such as

are denoted by headings G (general), A (academic),and T (technical).We can further classify these three

,

, and

;“
busi-

ness activities and events”such asgroupings into subgroups 1, 2, and 3, based on theirfrequency in the Commerce domain.

,(1) General vocabulary(G1, G2,G3＝HH,HM ,HL),,

; and “money/finance”such

, and
as

,

,

,

and

.

In general, these three lists contain the most high

These words in the T1 list look more like a “
lexis‑frequency, general‑purpose vocabulary in English,

for talking about business”rather than a “lexis forand are typically found in the GSL as well as in the

doing business”(Pickett, 1998) typically seen in theJSH.

A1 list. In other words, these words might be more

The G1 list contains a high number of function
words such as

, ,

,

, and

. In the G2 list,

there are some function words such as

andsuitable for students of economics rather than ofbusiness.

,

We might expect to find the majority of the T2

and many content words, e.g., nouns such aswords in a business environment. They are closelyand, verbs such as, adjectives

related to this discipline but may not be as strictlysuch asand, and adverbs such astechnical as T1. In the T3 list, some words might be. The G3 list includes mainly broader content
words such as

,

,

,

used in the business world, e.g. s

, and

.

,

, and

,
‑

,
. Sev-

eral are less directly related to business and a few(2) Academic vocabulary (A1, A2, A3＝MH, M M,

have no direct connection with business.

M L) Generally,these three lists are a mixture of the

4. Conclusion

three types of vocabulary (general, academic andtechnical).However,they seem to be characterized by
the presence of the words in the AWL (the AWL

Technical vocabulary is difficult to obtain using theconsists of words that are not included in the GSL but

traditional vocabulary selection criteria of frequency

that are used in a wide range of disciplines including

and range. Because the focus of these measures iscommerce).

ranking general‑purpose vocabulary in order of prior-

The majority of the words in the A1 list belong toity, separating technical vocabulary from general

the business world and the homogeneity of this group‑purpose vocabulary is still labor‑intensive,time‑con-is remarkable: “people” such as

suming,and heavily dependent on the selectorʼ
s exper-

,

,

and

,

,
,

,

;“
business events”such as,

the technical domain. English teachers generally do; “money/finance”such as

and
,

,

,

and

tise in English education and specialist knowledge of,

not have this specialist knowledge so there is clearly. Manya need for easy‑to‑use tools for identifying technicalwords (particularlyafter top 10)are words also includ-

vocabulary.

ed in the AWL, probably in the commerce discipline.

To provide such a tool, we introduced a particular

The A2 and A3 lists include a great deal of general

type of plot called wordplot that can be used to visual-‑purpose and academic vocabulary. Words in theseize the distribution of words in a specific domain inlists are not unique to a particular field and seem ofcomparison with their distribution in general English.

little interest from the viewpoint of selecting technical

We showed that words in the LH and LM regions invocabulary.

the wordplots of the Commerce and Natural Science(3) Technical vocabulary(T1,T2,T3＝LH,LM,LL)

domains,which occur frequently(high and medium)in

Examination of this extracted vocabulary shows thatthese domains but infrequently (low) in the whole

the “T”category are found at all three levels (1,2 andBNC, can be categorized as technical vocabulary. In3) and that the quantity of this type of vocabulary

future work, we want to apply wordplots to otherseems to decrease in order from 1 to 3.Almost all the

domains to further examine their usefulness inwords in the T1 list are purely business‑related “
spe-identifying technical vocabulary.

cialist vocabulary”and they display a specific busi― 19 ―11) Kenneth W. Church and Patrick Hanks. (1989)

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,

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― 20 ―

Appendix.

Statistics for the Natural Science word list (NSWL)App. Table 1 Top 10 words for the NSWL
LH

MH

HH

molecule

gene

the

particle

surfacebeinsectproteinofmousesequenceandelectrontemperatureapolymersolutionindolphindiseasetoclinicalequationthatmeasurement

complexhave

matrix

femaleit

LM

MMApp. Table 2 Number of individual words in eachregion for the NSWL
LH＝65
LM ＝253

MH＝176
MM ＝356

HH＝534
HM ＝165

775

LL＝456

M L＝242

HL＝76

774

774

774

775

2323

774

App. Table 3 Percent inclusion at rank 50fortheNSWL
Region

FWL

GSL

JSH

AWL

HH

90.0

100.0

100.0

0.0

HM

4.0

68.0

98.0

12.0

HL

10.0

64.0

90.0

10.0

HM

MH

0.0

48.0

60.0

20.0

0.0

34.0

48.0

22.0

spine

solid

student

MM

node

tail

association

ML

0.0

48.0

64.0

22.0

gravitational

suitable

performance

LH

0.0

10.0

14.0

4.0

linearcalculate

image

LM

0.0

14.0

16.0

8.0

ion

fully

pass

LL

0.0

8.0

14.0

2.0

mutanttypicalpurposesegment

largelytechnology

strandmetaltransfershieldconcept

experience

antibody

prevent

education

App. Table 4 Percent inclusion for the NSWL
Region

FWL

GSL

JSH

AWL

HH

22.3

78.5

93.6

11.0

HM

4.8

73.3

89.7

9.7

HL

6.6

69.7

88.2

13.2

MH

1.1

38.1

56.2

23.9

MM

1.4

36.2

58.1

21.1

ML

1.2

38.0

56.2

22.3

LL

ML

HL

X‑ray

entry

himself

proof

propertrade

thicknessyellowlaborabundant

electric

commission

temperate

library

lot

LH

0.0

10.8

18.5

4.6

vapor

locateeverything

LM

0.0

9.9

14.2

7.9

chronic

waste

investment

LL

0.0

4.6

12.3

6.6

liver

graduallyaim

cattlesummaryanyoneaccuracy

totally

chargeApp. Table 5 Relative recall for the NSWL
Region

FWL

GSL

JSH

AWL

HH

364.6

195.2

172.5

83.1

HM

79.3

182.4

165.2

72.9

HL

107.6

173.4

162.4

98.9

MH

18.6

94.7

103.6

179.4

MM

23.0

90.1

107.1

158.4

ML

20.3

94.6

103.5

167.8

LH

0.0

26.8

34.0

34.7

LM

0.0

24.6

26.2

59.4

LL

0.0

11.5

22.6

49.5

(H 18.12.20受理)
App. Fig.1 Wordplot of the NSWL
― 21 ―