論

日本大学生産工学部研究報告B
2007 年 ６ 月 第 40 巻

文

BNCビジネス語彙の抽出と e‑learning 教材の作成

中條清美 ・キャサリン オヒガン
内山将夫

・西垣知佳子・中村隆宏

Creating E‑Learning Material with Statistically‑Extracted Spoken & Written
Business Vocabulary from the British National Corpus
Kiyomi CHUJO , Kathryn OGHIGIAN , Chikako NISHIGAKI
Masao UTIYAMA
and Takahiro NAKAMURA

For many people, specialist vocabulary is a key element of ESP (English for Specific Purposes);
however,the use of conventional selection criteria (frequency and range)for identifying ESP vocabulary
has been found to be only partly successful. Because the focus of these measures is ranking general‑purpose vocabulary in order of priority,separating technical vocabularyfrom general‑purpose vocabulary is still labor‑intensive, time‑consuming, and heavily dependent on both the selectorʼ
s expertise in
English education and specialist knowledge of the domain,which English teachers generallydo not have.
To address this need,Chujo and Utiyama (2006)have established an easy‑to‑use,automated tool using
nine statistical measures to identify level‑specific, domain‑specific words. In this study, the log likelihood ratio was applied to the 1.32‑million‑word spoken business component and the 7.12‑million‑word
written business component of the British National Corpus.We examined the top 500 most outstandingwords of each list and confirmed that the log likelihood ratio identified intermediate‑level spoken andwritten business words bygrade level,and verified that the measure was effective in separating businessvocabulary from general‑purpose vocabulary. This study outlines a systematic way to create spoken
and written business vocabulary lists for a targeted proficiency level by using an established statisticalmeasure, and describes the development of an effective e‑learning program for Japanese college
students based on our previously published vocabulary teaching material for teaching TOEIC vocabulary.
Keywords: Business English, ESP, Specialist Vocabulary, Statistical M easures, E‑Learning Material

日本大学生産工学部教養・基礎科学系助教授
東京国際大学客員教授
千葉大学教育学部助教授
情報通信研究機構主任研究員
小学館
― 1―

,

effective in separating technical vocabulary fromgeneral‑purpose vocabulary, and provided the tem-

1. Backgroundplate for identifying business vocabulary for our curAs educators,we know vocabulary is the heart of alanguage (Beglar and Hunt, 2005: 7)

rent study.

and with

2. Purposes of the Studyadvances in corpus linguistics,we now have the tools
to create our own,real‑world vocabularylists.Corpus
linguistics is essentially studying language throughThe goals of this study were (1)to extract a spoken

existing texts or corpora. In other words, we as

and written business lexicon from the British

researchers can take a corpus of several million words

National Corpus (BNC) using nine statistical mea-

already existing in real‑world English,and analyze itsures; (2) to create intermediate level business wordin diverse ways−for example, what are the mostlists; and, (3) to combine our previously publishedfrequentlyused words in English,or what are the moste‑learning vocabulary teaching program for Japanesefrequently occurring collocations,or in the case of ourstudents with the business lists produced and refinestudy, what business words appear in a corpus (seethe program to maximize its effectiveness.

Table 1). Our challenge was how to take such a large

3. Methodology

corpus and find specific kinds of words, such as abusiness lexicon, and/or a targeted level of vocabulary.

3.1 The data

We know that applying conventional frequency and3.1.1 BNC spoken & written business sub‑corpora

range criteria to a corpus tends to extract general

The British National Corpus is one of the largest‑purpose vocabulary(Sutarsyah et al.,1994: 48) andelectronically‑accessible corpora consisting of overis therefore of limited use in identifying technical ESP

100 million written and spoken words in British Eng-

(English for Specific Purposes) words. Separatinglish (Burnard, 2000; Kennedy, 2003; Leech et al.,technical vocabulary from general‑purpose vocabu-

2001)

lary is still labor‑intensive, time‑consuming, andcomponents or sub‑corpora such as the sciences,liter-heavily dependent on the selectorʼ
s expertise in Eng-

ature, world affairs, arts, and commerce and finance.

lish education and a specialist knowledge of theAmong them are a 1.3‑million‑word spoken businessdomain (Chujo and Utiyama, 2006: 256) .

sub‑corpus and a 7.1‑million‑word written businessIn 2006, Chujo and Utiyama established an easy‑to‑use tool employing nine statistical measures to iden-. It is organized into various subject‑related

sub‑corpus and these have been selected as the basecorpora for this study.

tify level‑specific,domain‑specific words.TheyfoundThe procedure for preparing these business‑related

that specific statistical measures identified specificsub‑corpora for statistical applications is shown intypes of vocabulary. For example, the log likelihood

Fig.1. We first lemmatized all the words in bothratio (LLR) identified intermediate‑level technicalsub‑corpora to extract all base forms using thewords, and mutual information(MI ) identified

CLAWS7 tag set and created two alphabetical word

advanced level technical words.These measures werelists. Lemmatizing means we counted only one form

Table 1 Examples of English language corpora and usages
Examples of Observational

Examples of English Language Corpora

Language UsageBank of English(spoken & written English for COBUILD)
British National Corpus

frequently used words(spoken & written British English)

common collocations

American National Corpus

specialized words lists(spoken & written American English)
International Corpus of English(international varieties of English)― 2―

(2) The Longman

Business English Dictionary(Pearson Education Limited, 2000) includes ʻ
over
20,000 words and phrasesʼbased on the analysis of millions of authentic business texts.In this study,we usedonlyʻ
wordsʼ(totaling 4,565 entries)as an existing technical vocabularycontrol list to evaluate how effectively nine statistical tools extracted business words.
(3) The Living Word Vocabulary (Dale and Oʼ
Rourke, 1981) includes more than 44,000 items, and eachhas a percentage score that rates whether the word isfamiliar to students in U.S.grade levels 4 through 16.
Grades 13‑16 correspond to four years of university
Fig.1 Procedure for preparing BNC businesssub‑corpora for statistical applications

study.This list was used to determine the grade levelat which the central meaning of a word can be readily

for each word, for example ʻ
employ,ʼand noted theunderstood.

number of inflections such as ʻ
employs,ʼʻ
employed,ʼ(4) A junior and senior high school (JSH) textbook

and ʻ
employing.ʼThen,for pedagogical application,allvocabulary list containing 3,245 different base words

proper nouns and numerals were identified by theirwas compiled from the top selling series of JSH

part‑of‑speech tags and deleted manually and all

textbooks (the New Horizon 1, 2, 3 series and theunusual or infrequent words were eliminated by delet-

Unicorn I, II and Reading series)

ing words appearing fewer than 10 times in the spoken

Japanese high school students generally use these orlist and fewer than 100 times in the written list.

similar books to study English before entering a

Finally,this left us with a 2,780‑word spoken business

university. This vocabulary was used to subtract all

master list,and a 2,973‑word written business masterthe words taught in junior and senior high school andlist.

to confirm the business core words are unknown to

3.1.2 Control lists

university students.

We wanted not only to extract spoken and written

3.2 Statistical measures

in Japan.

business words by using statistical measures but also

Next,using the earlier study by Chujo and Utiyama

to evaluate how effectively this was done.In addition,(2006)as our template,we applied nine statistical tools

we wanted to know if these words appear generally into the spoken and written business master lists toEnglish at what［U.S.］native speaker grade level andextract and organize the vocabulary into more mean-

to determine which are learned by Japanese students

ingful lists. The nine tools used included simple

in the course of their junior and senior high school

frequency (Freq), the Dice coefficient (Dice) , Cosine

years. For these reasons, the following four control(Cosine) , the complementary similarity measure

vocabularylists were created byusing the same proce-(CSM ) , the log

dures described above in 3.1.1, and each vocabularychi‑square test (Chi2) , chi‑square test with Yatesʼslist is described in detail below:

correction (Yates) , mutual information (MI ) , andlikelihood ratio (LLR) , the(1) The British National Corpus High Frequency

McNemarʼs test (McNemar) . These statistical mea-

Word List (BNC HFWL), a list of 13,994 lemmatizedsures are widely used in computational linguistics.

words representing 86 million BNC words that occur

Using each measure, the statistical score for the

100 times or more (compiling procedure is detailed inextent of each wordʼs “
outstanding‑ness” (Scott,Chujo, 2004) , was used to determine if and how1999) in frequency of occurrence was computed.The

written business‑related words appear differently in aformula for each measure is available in Chujo andgeneral corpus.Similarly,the British National Corpus

Utiyama (2006) .

Spoken High Frequency Word List (BNC SHFWL),a

3.3 Extracting spoken and written business words

list of 8,462 lemmatized words representing 9.1 million

When each statistical tool is applied to the spoken

BNC spoken words, was used for spoken‑related

business or written business master list, it automati-

business words.

callyidentifyoutstanding words in frequencyof occur― 3―

Fig.2 Procedure for extracting BNC spoken & written business word lists

rence by making comparisons between the ESP words

4. Understanding the Meaning
Extracted Specialized Lists

and EGP words. Different statistics identify different
types of words.In this study,we applythe statistics toof theboth the sub‑corpora we created (the ESP Spoken andWritten Master Lists) and the original corpora (the

4.1 Extracted business words comparison

BNC SHFWL and BNC HFWL), and compare theIn order to get a clear picture for potential pedagog-

results (see Fig.2).These statistics indicate whether aical applications,we examined the top 500 outstanding

word is overused or underused in a specified listwords for each statistical measure. ʻ
Outstandingʼ

compared with a list of general English. In this way,

words indicate those words appearing near the top of

we can statistically determine how words in the tar-each list and which are ranked as outstandingly prom-

geted lists (in this case the business words) would

inent in terms of each statistical toolʼs criteria.Due toappear differently from words in a general corpus.

space limitations, Table 2 shows only the top 15 out-

Table 2 Top 15 extracted business words comparison
Spoken Business

Written Business

Rank
Freq

LLR

MI

Freq

LLR

MI

１

be

we

retrievethemarketlading２

themotionmeasurable

becompany

３

Iregionacquisition

of

bank

buyoutlong‑run４

youcongress

conductivity

to

the

arbitrage

５

to

union

inductionabusinesssubcontractor

６

andcolleague

civilize

and

price

stockmarket

７

that

clientincompetent

in

rate

offeror８

itcompany

feasibility

that

cost

９

we

train

upturnhave

firm

drafterno‑arbitrage

10

aconference

activistittax

shareholding

11

have

procedure

scandalforinvestment

headhunter

12ofbusinessthey

account

payout13

dopremium

marquisright‑hand

on

share

issuer

14

not

file

amalgamation

will

profitliquidity

15

in

trade

retention

thiscontractsalesperson

Frequency

31958

2440

22

15283842795

203

Word Length

2.4

6.5

9.3

2.8

5.6

8.9

― 4―

standing words with the extractions from three of thenine tools from the two business corpora (spoken andwritten). This snapshot provides a clear and simple
illustration of the types of words extracted by eachmeasure.
We can see that the lists are verydifferent from one
another even though they were extracted from thesame spoken and written business master lists. For
example, in the written LLR list, we can recognize
fairly simple business words such as market, company,
and bank,while in the written MI business list we cansee more complex words such as lading, buyout, arbitrage, and subcontractor.
The bottom two rows of each column show theaverage frequency score and average word length ofthese 15 words. As we see from Table 2, the averageFig.3 The overlap of the top 500 extractions with
the Longman Business English Dictionary

frequency score decreases from left to right or fromouter line shows the overlap with the top 500 written

Freq to MI . On the other hand, the average word

business words. We can see the nine tools effectively

length increases from Freq to MI ,ranging from 2.4 orproduced relevant technical vocabulary.In particular,

2.8 to 9.3 or 8.9 letters. Although we are aware that

we see that there is a 67% overlap between the LLR,

word difficulty may be influenced by many more

Chi2 and Yates spoken word list and the dictionary

factors than frequency and word length, this might

entries. For the written business words,79% of LLR,

support the possibility that specific statistical tools

Chi2 and Yates and 80% of the MI words overlap

can be used to target specific grade level vocabulary.

with the dictionary entries.

This will be explored in the following sections.

4.3 Grade level based on word familiarity

4.2 Business English dictionary entry word overlap

Next,we investigated at what U.S.grade level theFirst, to evaluate how effectively these tools

top 500 words would be understood bynative speaking

extracted technical (business) words, they werechildren. For comparative data, we used Dale andcompared to an existing technical (business) vocabu-Oʼ
Rourkeʼs 1981 data published in The Living Wordlary control list. For this we used the 4,565 entry

Vocabulary.In looking at the horizontal line in Fig.4,

words in the Longman Business English Dictionary.In

we can see that 80% of the words from Freq, Dice,

Fig.3, the inner line in the radar chart shows the

Cosine, and CSM are generally understood by 6th

overlap in percentage between the business dictionary

grade students; LLR, Chi2 and Yates are known by

entries and the top 500 spoken business words,and the

8th to 10th grade students; and 80% of the MI andSpoken Business

Written BusinessFig.4 U. S. grade level comparisons for the top 500 words― 5―

McNemar words are known by 12th or 13th gradestudents (college freshmen).
In terms of practical pedagogical application, we
inferred from this data, in addition to several of ourprevious similar studies (Chujo and Utiyama, 2005;
Chujo and Utiyama, 2006; Chujo, Utiyama andOghigian, 2006),

that (1) the business words

extracted by Freq, Dice, Cosine and CSM might bemost useful for beginner level EFL learners; (2) the
LLR, Chi2 and Yates lists might be most useful forintermediate level EFL learners; and (3)the MI and
McNemar vocabulary might be most appropriate foradvanced level EFL learners.

5. Developing an E‑Learning
Building Program

Vocabulary

Fig.5 Developing three intermediate‑level businessword lists

5.1 Intermediate‑level word selection
Since our focus in this study is intermediate levelbusiness vocabulary, we chose the LLR lists as thetarget lists for the following reasons: 1) LLR was
effective in extracting business words as shown bythe
overlap with business dictionary entry words; 2) it
extracted appropriate business words for intermediate level words in terms of word familiarity; 3) it isFig.6 Core vocabulary for Business Vocabulary 1
irrelevant words.

“a well‑established statistical technique … andFrom these 455 words, we created lists of “
core

behaves well whatever the corpus size”according towords.”As shown in Fig.6, we chose, according to

Oakes (1998: 174) ; and 4) it is one of the mosteach wordʼs score ranking,40 words appearing in bothwidely used statistical tools in corpus linguistics (seethe spoken and written contexts as business core

Chujo and Utiyama, 2006: 256‑257) .

words. We also chose 40 core spoken words and 40

To further refine the intermediate business vocabu-

core written words based on each wordʼ
s LLR scorelary from each of the top 500 spoken and written

ranking. In this study, these 120 words will form thebusiness words, we next subtracted all the known

basis of the “Business Vocabulary 1” e‑learningwords taught in Japanese junior and senior highmaterial,and the remaining 335 words will be used forschool (JSH words)(see Fig.5).That gave us 71 coredeveloping “Business Vocabulary 2”and “Businessbusiness words that appeared prominently in both

Vocabulary 3.”

spoken and written business contexts and that would

5.2 Building an e‑learning program

be new to high school graduates. In addition, 190 andArmed with our vocabulary lists, we then consid-

194 words appeared prominently in a spoken andered how we could present them in a framework thatwritten business context respectively,yielding a total

would best support vocabulary learning. We wantedof 455 words.

to integrate theories of learning,information process-The goal in using statistical measures is to narrowing, second language acquisition and TESL to ensure

down the number of candidates for the targeted tech-long‑term retention and create an enjoyable learning

nical word list, but it is not meant to be a definitive

atmosphere.

list. These statistical tools can help users to selectTaking advantage of the possibilities inherent intechnical vocabulary automatically and reduces thecomputer‑assisted language learning, we have beenneed for expertise in that field. By using extracted

incorporating various kinds of specialized vocabulary

lists such as these, users can easily manually deletesuch as TOEIC and TOEFL with a variety of exer-― 6―

cises into an original vocabulary teaching methodin the unit are provided.In Step 1,a list of the target

previously published (Chujo, 2002; Chujo et al., 2002,

words is presented. In Step 2, students learn the2003,2004).We noted from a previous imple-

correct pronunciation and the Japanese equivalent of

mentation of this prototype software with other types

each target word. In Step 3, two phrases for eachof vocabulary that students enjoyed learning with thistarget word are learned with their pronunciation. In

program and that it allowed for a high retention rate

Step 4, learners double‑check their understanding offor the acquired target words. Because this program

the target words byrecalling the spelling of the words.

has been shown to be an effective tool for learning

Finally, in Step 5, learners listen to and transcribe

vocabulary and improving communicative proficiency

each phrase in their notebooks with a pencil.

as measured by the TOEIC (Chujo et al., 2003) , weThis “Business Vocabulary 1”e‑learning material

have used it as our template for this intermediate

was created using Homepage Builder. Pictures werebusiness vocabulary learning material.

purchased from Shutterstock.com Support (http://

This e‑learning program has 12 units of ten words
each,so students learn 120 words in total.As is shownwww.shutterstock.com) , and the voice recording
was done at our university studio.

in Table 3, they learn 10 target words for each unit

The phrases for each target word were retrieved

using theʻFive Learning Stepsʼwhich are organized tofrom the corresponding BNC sub‑corpora using theteach target words first in isolation (Steps 1,2,and 4),

Shogakukan Corpus Network (http://scn02.corpora.
˜sakura03/) , and two phrases were chosen withjp/

and then in phrases (Steps 3 and 5).
In Fig.7, example pages from this program arerespect to occurrence of frequency and the brevity ofshown.In the Introduction,pictures depicting suitable

examples. In other words, we chose “real”phrases

business scenes corresponding to the ten target words

directlyfrom the BNC,selecting those which appeared
most often and were the most concise.Both words andTable 3 Five learning steps

phrases are provided with Japanese translations using

Introduction Step 1 Overview of 10 new words

A Dictionary of English Usage for Business andStep 2 Sounds & Meanings of 10 words

Finance (Hashimoto, 1991)

Step 3 Sounds& Meanings of20phrases

with a specialist in financing. Because there may be

Learning

Follow‑up

Step 4 Spelling of 10 words
Step 5 Dictation of 20 phrases

as well as consultingsome variation in what users will select, we havelisted the 120 business words and their 240 businessphrases from “Business Vocabulary1”in theAppendix.

Introduction

Step 1

Step 2

Step 3

Step 4

Step 5

Fig.7 Example pages from the “Business Vocabulary 1”― 7―５) Kennedy, G. (2003) Amplifier collocations in the

6. Further Research

British National Corpus: Implications for English language teaching. TESOL Quarterly, 37 (3),467‑487.

In this study, we hoped to demonstrate how easily
technical (business)vocabulary can be extracted from

６) Leech,G.,Rayson,P.and Wilson,A.(2001) Word

corpora and to provide other educators with the tools

Frequencies in Written and Spoken English. Har-

to do so themselves. The selected intermediate busi-

low: Pearson Education Limited.

ness words created in our study are available
˜chujo.
electronically at http://www5d.biglobe.ne.jp/

７) CLAWS7 (1996) http://www.comp.lancs.ac.uk/

The “Business Vocabulary 1”e‑learning material is

８) Chujo, K. (2004) Measuring vocabulary levels ofavailable at http://weekend.kir.ne.jp. “Business

English textbooks and tests using a BNC lem-

Vocabulary 2”and “Business Vocabulary 3”are cur-matised high frequency word list.In : Nakamura,

rently in development.

J., Inoue, N. and Tabata, T. (Eds.), English Cor-

computing/users/eiamjw/claws/claws7.html.

Further research will focus on implementing thispora under Japanese Eyes. Amsterdam : Rodopi,

e‑learning material as a case study in university‑levelpp.231‑249.

English classes in Japan to evaluate its effectiveness９) Pearson Education Limited. (2000) Longman

and to obtain feedback from students to help refine

Business English Dictionary. Harlow: Pearson

the material in order to promote better retention,

Education Limited.
10) Dale,E.and Oʼ
Rourke,J.(1981)The Living Wordusability and motivation.

Vocabulary. Chicago : World Book‑Childcraft

Acknowledgement

International, Inc.
11) Asano,H.,Shimomura,Y.,Makino T.,Ikeda,M.,

This studyis funded bya Grant‑in‑aid for Scientific

Ikeya, A., Ishizuya, S., et al. (1999) New Horizon

Research (No.17520401) from the Japan Society for

English Course 1, 2, and 3. Tokyo : Tokyo

the Promotion of Science and M inistry of Education,

Shoseki.

Science, Sports and Culture. It was also supported in

12) Suenaga, K., Yamada, Y., Fukai, K., Nakamura,part by the College of Industrial Technology, Nihon

S.,Ishizuka,K.,Ichinose,K.,et al.(2001)Unicorn

University.

English Course I, II , and Reading. Tokyo :
Bunʼ
eido.

＊Part of this studyis based on a presentation given at13) Manning, C. D. and Schutze, H. (1999) Founda-

the Fourth Asia TEFL International Conference,

tions of Statistical Natural Language Processing.

August 18, 2006, in Fukuoka, Japan .

Cambridge: The M IT Press.
14) Manning, C. D. and Schutze, H. (1999) Founda-

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Appendix

Business Vocabulary 1
Top 40 Spoken & Written Business Vocabulary

No.
１

Word

Phrase 1

Phrase 2

account

口座

a bank account

銀行口座

a profit andlossaccount 損益勘定

２

investment

投資

a capital investment

資本投資

a business investment

事業投資

３

profit利益

profit before tax

税込み利益

a net profit

純利益

４contract

契約

an employment contract 雇用契約

a futures contract

先物契約

５

financial

財務のfinancial management 財務管理

a financial problem

財務問題

６

customer

顧客

customer service

顧客サービスcustomer satisfaction

顧客満足

７

management

経営（陣），管理

top management

最高経営層，首脳部

system management

システム管理８

employee

従業員

a government employee 政府職員

an employee shareownership plan

従業員持株制度client10 organization

顧客

a client group

顧客グループa client company

取引先会社

組織，団体

a social organization

社会組織

an internationalorganization

国際組織

11 shareholder（英）株主

a minority share- 少数株主
holder12 buyer

買い手buyers and sellers

買い手と売り手a first‑time buyer

初めて不動産を購入する人

13 payment

支払い

an interest payment

利息支払い

a monthly payment

月払い

14 cash現金

a cash flow

現金収支

a cash payment

現金支払い

15 sector

部門

the private sector

民間部門，私的セクター

the public sector

公共部門，公的セク
ター

16 liability
17 fund責任，義務

the tax liability

納税義務

the product liability

製造物責任

基金

a pension fund

年金基金

a trust fund

信託基金

18 audit

会計監査

an internal audit

内部監査

an audit report

監査報告書

19 insurance

保険

a lifeinsurancecompany 生命保険会社

health insurance

健康保険

20 policy

政策，保険証券

an economic policy

経済政策

an insurance policy

保険証券

21 union（労働）組合a trade union

労働組合

a national union

全国組合

22 corporate

法人のcorporate finance

企業金融

corporate body

法人

23 employer

雇用者，事業主

a manufacturer
employer

製造業事業主a relationship between 雇用者対従業員の関
employer and employee 係

24 executive

管理職のa chiefexecutiveofficer 最高経営責任者

an executivecommittee 経営委員会

25 accountant

会計士a chartered accountant 公認会計士

a trainee accountant

会計士補

26 supplier27 employment

供給者

a software supplier

ソフトウェア製造業者

supplier companies

下請け会社

雇用，職業

an employment
opportunity

雇用の機会

an employment agency 職業安定所

28 objective

目標，客観的なthe main objective

中心的な目標an objective fact

29 competitive

競争のa competitive market

競争市場

a competitiveadvantage 競争上の優位性

30 fee手数料

a license fee

特許権使用料a membership fee

会費

31 premium

保険料，割増金a premium rate

保険料率a premium price

特別料金

32 earnings

かせぎ高

export earnings

輸出収益

lifetime earnings

生涯所得

33 target

目標

a sales target

販売目標

a target market

目標市場

34 acquisition

買収，取得

merger and acquisition 企業の合併・買収

an acquisition of acompany会社の取得

35 delivery

配達a delivery date

受け渡し日

a delivery service

配達サービス36 procedure

手順，手続きa new procedure

新しい手順

a procedures manual

手続き便覧

37 recession

景気後退

an economic recession 景気後退

recession proof

景気後退の影響を受けない

38 expenditure

支出

capital expenditure

設備投資

39 option

選択，選択権a share option（英）自社株購入選択権

40 pension

年金

a pension fund

年金基金

９

― 10 ―an ordinaryshareholder 普通株主

客観的事実

government expendi- 財政支出
ture
楽な選択，無難な方
a soft option
a pension plan

年金制度

Top 40 Spoken Business Vocabulary
No.

Word

Phrase 1

Phrase 2

１ motion動議

support the motion

動議をサポートする

propose a motion

２ region
３ colleague

地域

a different region

異なる地域

同僚

a former colleague

かつての同僚in the region of $2,000 2,000ドル付近に同僚の男性
male colleagues

４ conference

会議

a conference call

電話会議

hold a conference

会議を開く

５ fileファイル

a data file

データのファイル

a file name

ファイルの名前６ composite

複合のa composite material

複合材料

a composite tax rate

総合税率

７ delegate

代表者，
委任する

a conference delegate

会議の代表者

delegate authority to

権限を委譲する

８ spreadsheet

スプレッドシート

a spreadsheet program スプレッドシートプ
ログラム

a spreadsheet file

スプレッドシートファイル

９ activist

活動家

an environmental 環境保護活動家
activista human rights activist 人権保護活動家

10 feedback

感想や意見

positive feedback

11 formally

正式にa formallyagreed price 正式に同意された価格Itʼs formally seconded. 正式に支持されました

12 voluntary

任意のa voluntaryorganization ボランティア組織

a voluntary worker

13 manual

人力の，手引きa manual worker

作業員

an instruction manual 操作マニュアル，取扱説明書

14 membership

会員（地位）

a membership fee

会費

a full membership

正会員

15 workplace

職場

safetyin theworkplace 職場の安全

his former workplace

彼の以前の職場16 assertive

断定的なassertive behavior

自己主張の強い振る舞い

an assertive person

自分に自信を持った人

17 regional

地域のregional development

地域開発

a regional office

地域事務所

18 resolution

決議

adopt a resolution

決議を可決する

pass a resolution

決議を可決する

19 directory

住所氏名録

a telephone directory

電話帳

a data file directory

データファイルディ
レクトリ

20 rep代理人

a sales rep

セールスマン，販売員，販売代理店

a safety rep

保安職員

21 formula式，公式

use a formula

定式を使う

a standard formula

標準式

22 appraisal

評価

an appraisal system

評価システムan appraisal report

評価報告

23 secretary秘書

a company secretary （会社の）秘書

a private secretary

個人秘書

24 print

印刷

the small print（契約書の）細字部分（不利な条件）in print

発売中，出版されて25 description

記述，説明書a job description

職務内容記述書，職務，職種a trade description（英）取引表示（法）26 referral

紹介make a referral（求人先に，専 門 医に）紹介するa referral service

参照サービス

27 waiver

権利の放棄

a collision damage （レンタカー）車両保険，免責軽減制度
waiver28 diary

日誌a personal diary

29 task

課せられた仕事， a task force
タスク

正のフィードバック

get feedback

動議を出す

反響を調べる

ボランティアa waiverofthepremium 保険料払い込み免除

私的な日誌

a desk diary

卓上日記

機動部隊，プロジェ
クトチーム

a task group

タスクグループ，任務群

a self‑employedbuilder

自家営業の建設業者

30 self‑employed 自家営業の

a self‑employed per- 自家営業者
son

31 review（再）調査

under review

調査の対象となっている

a review committee

調査委員会

32 agenda

議題

on the agenda

協議事項となって

an agenda item

協議事項

33 session

開会していること，課業a training session

訓練期間

in session

開会中，会議中

34 aggressive

攻撃的な，積極的な

aggressive behavior

攻撃的な行動

an aggressive market- 積極的な販促
ing campaign

35 consignment

委託（販売）a consignment ofgoods 商品の委託販売

36 setup

セットアップ，準備，設定

37 investigator
38 salesman

a consignment note

受託書

a setup program

セットアッププログ
ラム

a setup screen

セットアップ画面

調査者

a private investigator

私立探偵

an accident investigator 事故取調官

セールスマンa car salesman

車の外交員

an insurance salesman 保険勧誘員

39 sponsor

スポンサー，広告主

40 binder

バインダー

a major sponsor of the オリンピックの主要スポンサー
Olympics
リングバインダー
a ring binder― 11 ―

an officialsponsorform 公式スポンサー用紙
a loose‑leaf binder

ルーズリーフバイン
ダーTop 40 Written Business Vocabulary
No.
１

Word

Phrase 1

Phrase 2tax

税

a local tax

地方税

a sales tax

売上税

２

income所得an income tax

所得税

a net income

純所得

３

asset

資産

net assets

純資産

fixed assets

固定資産

４

investor

投資家

individual investors

個人投資家

overseas investors

海外投資家

５

credit

信用貸し

a credit card

クレジットカードa tax credit

税額控除

６

finance

財務，金融

corporate finance

法人金融international finance

国際金融

７

dividend

配当

a final dividend

年度末配当，最終配
当

an interim dividend

中間配当

８

transaction

取引a commercialtransaction 商取引

９seller

売れる物

a goods seller

売れ行きの良い商品

direct sellers

直販業者

a penalty clause

違約条項

a financial transaction 金融取引

10 clause条項

an exclusion clause

除外条項

11 bond債券

a government bond

国債

a bond market

債券市場

12 debt
13 equity

負債

a bad debt

不良債権

the national debt

国債

財産物件の純粋価格

equity finance

株式発行による資本
調達

equity capital

自己資本

14 auditor

監査役

the companyʼs auditors 会社の監査役

an auditorʼ
s report

監査報告書

15 inflation

インフレーションthe inflation rate

インフレ率

the high inflation

高いインフレ率

16 revenue

歳入，収入

a tax revenue税収

a sales revenue

総売上高

17 currency

通貨

a foreign currency

外貨

a single currency（複数の国の）単一通貨

18 property

財産

an intellectualproperty 知的財産権

19 merger

合併

a merger agreement

合併の同意

a merger plan

合併案

20 requirement

必要条件，要件a legal requirement

法的な必要条件

a safety requirement

安全要求事項21 discount割引

a cash discount

現金割引

a discount rate

値引率22 mortgage

住宅ローン

a mortgage rate

住宅ローン金利a mortgage payment

住宅ローン支払い

23 purchaser

購買者

a potential purchaser

潜在購買者

a prospectivepurchaser 見込み客

24 competition

競争

an international com- 国際的な競争
petition

25 takeover買収

a hostile takeover

26 marginal
27 portfolio

限界のa marginal cost

所有有価証券，金
融資産

28 monetary

通貨のa monetary policy

29 chairman

議長

a committee chairman 委員長

30 valuation評価

a property valuation

31 dealer

売買人

32 retailer

小売業者

33 overseas（店舗，a commercial property 営業用不動産
事務所など）

a price competition

価格競争

敵対的買収

a takeover bid

株式公開買付け
（TOB）

限界費用

a marginal tax rate

限界税率

a portfolio investment 証券投資a portfolio manager

資産管理担当者

the internationalmonetary system

国際通貨制度a board chairman

取締役会長

財産評価

a stock valuation

在庫品評価

a car dealer

車の販売業者

a wholesale dealer

卸売商

a food retailer

食料品店

a small retailer

小規模小売店

海外の

an overseas market

海外市場

an overseas branch

海外支店

34 lender

貸し手a money lender

金貸し，金融業

lenders and borrowers 貸し手と借り手

35 breach違反

a breach of contract

契約不履行

a breach of trust

背任36 sterling

英貨の，ポンドのa pound sterling

英貨１ポンドa sterling balance

ポンド残高

37 accountancy

会計士

an accountancy firm

会計事務所

an accountancyservice 会計監査業務

38 provision

供給

a provision of services サービスの供給

the provision of
appropriate training

the market equilibrium 市場のan equilibrium price39 equilibrium衡

40 liquidity

流動性

the internationalliquidity

通貨（金融）政策

衡状態

国際流動性

a liquidity ratio

適切な訓練の供給
衡価格
流動性比率(H 18.12.19受理)― 12 ―