Extracting Level-Specific Science and Technology Vocabulary
from the Corpus of Professional English (CPE)
Kiyomi CHUJO, Ph.D.
College of Industrial Technology
Nihon University
chujo@cit.nihon-u.ac.jp

Masao UTIYAMA, Ph.D.
National Institute of Information and Communications Technology
mutiyama@nict.go.jp

Takahiro NAKAMURA
Shogakukan Inc.
takahiro@shogakukan.co.jp

Abstract
With rapid advances in technology come rapid advances in the language of technology,
or English for Science and Technology (EST). We have had success in our earlier
research in devising a systematic means of extracting level- and domain-specific words
from the British National Corpus. In this study, we apply a similar methodology to the
Corpus of Professional English (CPE), a 20-million-word computerized database ofEnglish used by professionals in science and technology in twenty-two domains such as
biology, chemistry, engineering, mathematics, medicine, and physics. This study
describes the procedure for extracting multi-level EST vocabulary from the CPE byusing nine statistical measures, and an examination of the top 500 most outstandingwords produced by each statistical application by grade level and EST dictionary entry
word coverage.

1. Introduction
With rapid advances in technology come rapid advances in the language oftechnology, or English for Science and Technology (EST). This kind of English isimportant not only in scientific and technological activities but also in universities,
which, increasingly, find themselves responsible for providing EST-related English
skills to an ever-expanding population of science and technology students. EST courses
are usually taught to seniors and graduate students and are designed to inculcate-1-students with an ability to read and write the scientifically and technologically oriented
English that they are likely to encounter in their professional careers. Consequently,
technical articles from professional journals are often used in lieu of a textbook.
One of the prominent aspects of the linguistic knowledge needed to comprehend
specialized texts is the corresponding specialized vocabulary or “technical words thatare recognizably specific to a particular topic, field, or discipline” (Nation, 2001: 198).
EST vocabulary development is essential in order to achieve proficiency in EST, andcurrent research is now turning to developing EST vocabulary lists for teachers,
students, and researchers.
2. Review of the literature
2.1 EST word lists and corpora
Although various word lists, such as A General Service List of English Words (GSL)(West, 1953) and the Academic Word List (Coxhead, 2000) have been developed forassisting vocabulary learning, as Kuo (2007) notes, not many EST word lists have been
compiled.
Flood and West (1953) devised a ‘supplementary scientific and technical vocabulary’to the GSL. It is a definition list of 425 words “expressing all ordinary scientific andtechnical subjects within the limits of non-specialized study” (West, 1953: 583). Knightand Bethune (1972: 505) compiled a list of 277 ‘science words students know’ toprovide teachers “evidence of the recognition of selected science words by students ingrades 7, 8, 9, 10, 11, and 12.” Puangmali (1976) collected 500-550 words from ten
engineering texts (totaling 5,202 words) to compile a 1,148-different-word engineering
English vocabulary list.
As corpus linguistics has advanced, efforts to create larger EST corpora which cover
more EST domains have been made. Some examples are the Jiaotong Daxue Computer
Corpus of Texts in English for Science and Technology (JDEST), which “comprises
2000 texts of about 500 words each --- one million words in all” (Yang, 1985: 24-25),
the Student Engineering English Corpus (SEEC) of nearly two million words built to“represent the engineering lexis encountered in English-language textbooks in basic
engineering disciplines” (Moudraia, 2004: 139), and the Scientific Research Word List
(SRWL) of 420 word families compiled from a corpus of 1.84 million words consistingof 360 journal articles in ten scientific research fields by Kuo (2007).
The largest current EST corpus is the Corpus of Professional English (CPE) which
was compiled by the Professional English Research Consortium (PERC) established in
2002. The CPE consists of a 20-million-word computerized database of English used by-2-

professionals in science and technology in twenty-two domains such as biology,
chemistry, engineering, mathematics, medicine, and physics. Based upon the 2001
Journal Citation Reports, 3,739 files of 50,000 words per text were collected from the
top 20 percent of the journals listed in terms of impact factor rating within eachcategory1. The CPE corpus is still in development, but fortunately, the first phase trial
version provides important language data.
2.2 Selecting EST words
We know from Sutarsyah et al. (1994) that selecting specialized vocabularies by using
the traditional criteria of frequency and range is only partly successful. Because thefocus of these measures is ranking general-purpose vocabulary in order of priority,
separating specialized vocabulary from general-purpose vocabulary is still laborintensive, time-consuming, and heavily dependent on the selector’s expertise in English
education and specialist knowledge of the domain, which English teachers generally
may not have.
A number of corpus-based studies have used certain statistical measures to identify
specialized vocabulary. For example, Nelson (2000) used the log-likelihood statisticfrom WordSmith Tools to find words that are statistically more frequently used inbusiness English than in general English by comparing each word’s frequency in abusiness English corpus with its frequency in the British National Corpus (BNC). Chujo
and Utiyama (2004) and Utiyama et al. (2004) established an easy-to-use tool
employing nine statistical measures to identify level-specific, domain-specific words
from a corpus. Subsequently, Chujo and Utiyama (2005) created a list of written science
vocabulary by applying those nine statistical measures to the 7.37-million-word written‘applied science’ component of the BNC. It was found that each measure extracted adifferent level of domain-specific words by vocabulary level, grade level, and school
textbook vocabulary coverage and that specific measures produced level-specific words,
i.e. the log likelihood ratio (LLR) identified intermediate-level specialized words, andmutual information (MI) identified advanced level specialized words. These measures
were effective in separating specialized vocabulary from general-purpose vocabulary,and provide a useful template as a means of identifying EST vocabulary. A close
examination of the extracted words, however, shows that the applied science domain of
the BNC gives much more weight to computer science technology and the medical field.
Clearly we need a corpus representing a broader range of contemporary EST domains.
1

The impact factor is a measure of the frequency with which an ‘average article’ in a journal has
been cited in a particular year or period.
http://scientific.thomson.com/free/essays/journalcitationreports/impactfactor/-3-

In summary, a review of the literature shows that there is a need for an EST
vocabulary list based on a more representative corpus. Secondly, it is possible toidentify EST vocabulary by using statistical measures such as the LLR and MI. Finally,
the 20-million-word CPE provides an excellent basis for the development of this kind of
EST list.
3. Purposes of the Study
The purposes of this study are (1) to extract various levels of EST words by applying
nine statistical measures to the 20-million-word CPE; (2) to identify the proficiency
level of the EST words based on U.S. native speaker grade level; and (3) to determine
the effectiveness of these measures by comparing the extracted words to an existing
EST vocabulary list.
4. Procedure
4.1 The Data
4.1.1 CPE Master List
The CPE corpus has twenty-two sub-corpora shown in Table 12. The procedure forpreparing the CPE master list for statistical application is as follows.
Agriculture

Food Science

Biology

Forestry

Chemistry

General Science

Civil Engineering

Materials Science

Computer Science

Mathematics

Construction & Building Technology

Medicine

Earth Science

Metallurgy & Metallurgical Engineering

Electrical & Electronic Engineering

Nuclear Science & Technology

Engineering

Oceanography

Environmental Sciences

Physics

Fisheries

Telecommunications

Table 1 The Twenty-two CPE Domains

2 Permission to use the first trial version of the CPE was granted by PERC. It is anticipated thatthese sub-corpora will be revised and expanded and the CPE corpus will be released in the near
future.
-4-

We first created a lemmatized list from the CPE corpus using the CLAWS7 tag set3 to
extract all base forms. Next, if a word appeared fewer than 100 times in the corpus, it
was deleted. Thus, all unusual or infrequent words were eliminated. Finally, all proper
nouns and numerals were identified by their part of speech tags and deleted manually
since statistical measures mechanically identify these words as technical words (Scott,1999). This process yielded a 4,467-word CPE master list, representing 13,527,303
words.
4.1.2 Control Lists
We wanted not only to extract EST words but also to understand the proficiency level
for these words, based on [U.S.] native speaker grade level. Furthermore, we wanted toevaluate how effectively the extraction was done in terms of identified EST words, andif so, to what extent. For the purpose of comparison, control vocabulary lists werecreated using the same procedures described above in 4.1.1. These control lists aredescribed in detail below.
(1) The British National Corpus High Frequency Word List (hereafter BNC HFWL) is alist of 13,994 lemmatized words representing eighty-six million BNC words that occur
100 times or more. (For the compiling procedure, see Chujo, 2004). It was used forcomparison to statistically determine how each of the domain-specific words in our
CPE master list would appear differently from words in a general corpus.
(2) The Living Word Vocabulary (Dale and O’Rourke, 1981) is a list that includes morethan 44,000 items, and each has a percentage score to correlate word familiarity to[U.S.] students’ grade levels four through sixteen. For supplementing grade levels one
through three, reading grades from Basic Elementary Reading Vocabularies (Harris andJacobson, 1972) were used. These lists were used to determine the grade level at which
the central meaning of a word can be readily understood. Of course, using more recent
data would be desirable, however, to our knowledge, no such data is available.
(3) The Concise Illustrated Dictionary of Science and Technology (Gibilisco, 1993: viii)
includes “more than 5,500 of today’s most commonly used scientific and technicalterms.” It is “designed especially for high school and college students who needaccurate information on a broader range of subjects.” In this study, we used 5,640
entries as an existing technical vocabulary control list to evaluate how effectively ninestatistical tools extracted EST words. Using a larger (5,000-10,000 word) and more
recent publication would be ideal; however, other publications, such as Chambers
3

http://www.comp.lancs.ac.uk/computing/users/eiamjw/claws/claws7.html-5-Dictionary of Science and Technology (Walker, 1999) containing over 50,000 entries,
are too large for our limited resources. Within the confines of this study, it was notpossible to create a control list from such a massive database.
(4) West’s (1953) A General Service List of English Words containing the 2,000 mostbasic words from the Interim Report on Vocabulary Selection (Faucett et al., 1936) thatare considered necessary for learning English as a foreign language was compared tothe extracted lists, as was the Function Words from Nation (2001: 430-431) containing
320 words. Function words express grammatical relationships with other words within asentence. They may be prepositions, pronouns, auxiliary verbs, conjunctions,grammatical articles or particles.
4.2 Identifying Outstanding EST Words
4.2.1 Statistical Measures
We used nine statistical measures: simple frequency (Freq), the Dice coefficient(Dice) and Cosine (Cosine) (Manning and Schütze, 1999), the complementary similarity
measure (CSM) (Wakaki and Hagita, 1996), the log likelihood ratio (LLR) (Dunning,1993), the chi-square test (Chi2) and chi-square test with Yates’s correction (Yates)(Hisamitsu and Niwa, 2001), mutual information (MI) (Church and Hanks, 1989), andMcNemar’s test (McNemar) (Rayner and Best, 2001)4. A detailed description of eachmeasure can be found in Utiyama et al. (2004) and Chujo and Utiyama (2006), and thenotation for these kinds of statistics can be found in Scott (1997).
4.2.2 Statistical Application
These statistical measures are widely used in computational linguistics. They
automatically identify the most outstandingly frequent words in a given specified list (inthis case, the CPE master list) by comparing each word’s frequency with a control list
(the BNC HFWL). In other words, these statistics indicate whether a word is overusedor underused in a specified list compared with a list of general English. We want todetermine those words that ‘stand out.’ The statistical score of a word, i.e. the extent of
the dissimilarity between two word lists, is calculated by comparing the patterns of thefrequency of each word in the CPE word list with the frequency of the same word in the
BNC HFWL.
Using each measure, the statistical score for the extent of each word’s “outstandingness” (Scott, 1999) in frequency of occurrence is computed. Since each measure uses a
4 The formula for each measure is available on the web at http://www5d.biglobe.ne.jp/~chujo/ or

http://www2.nict.go.jp/jt/a132/members/mutiyama.
-6-different formula, it gives a different score to each word. The words are sorted from themost outstanding to the least outstanding by their statistical ranking. Thus the words
near the top are ranked as very outstanding in terms of each statistical measure’s criteria.
The goal in using these measures is to narrow the number of candidates for the EST
word list, but it is not meant to be a definitive list. These statistical tools can help users
to select technical vocabulary automatically without a great deal of specialist knowledge.
With the extracted lists, users can easily manually delete irrelevant words.
5. Results and Discussion
5.1 Identifying the Vocabulary Levels of the Extracted Lists
5.1.1 Top 50 Extracted Words Overview Comparison
The top 50 words from each of the nine measures in descending order are shown in
Table 2. Since the top 50 extractions made using Freq and Dice and for Chi2 and Yates
were almost the same, this data appears as two columns (Freq/Dice and Chi2/Yates)
rather than as four separate columns. A glance at the top 50 words gives a brief
overview of the general tendencies inherent in the extraction of the nine measures.
The lists in Table 2 are different from each other even though they were extracted
from the same corpus. The top 50 words identified by Freq and Dice include general
vocabulary such as the and of that usually appears at the top of high frequency lists, aswell as some essential EST words more common in this field than others such as result,cell, model, and data. For Cosine and CSM, the top 50 extractions include many
essential EST words such as temperature, concentration and surface. For LLR, Chi2 andYates, the top 50 extractions include a greater number of EST words. Both the MI and
McNemar lists identify higher-level EST words. MI extracted important advanced level
EST words such as aqueous, oxidation, substrate and biomass. McNemar extracted
lower frequency words such as seabed, deuterium, and angina. Overall, word length
and word difficulty appear to increase from left to right. This will be explored in thefollowing sections.

-7-Freq / Dice

CosineCSM

LLR

Chi2 / Yates

MI

McNemar

1

the

the

the

of

cell

aqueousseabed

2
3
4
5
6
7
8
9
10

of
be
and
in
a
to
for
that
with

of
be
and
in
a
to
for
this
with

of
in
be
and
use
this
for
by
with

cell
the
model
data
sampleresultuse
temperature
protein

of
model
data
sampletemperaturetheresultproteinconcentrationoxidation
substratebiomass
latencylipid
purifydeformation
wild-typeanterior

deuterium
angina

11
12
13
14
15
16
17
18
19
20

this
by
have
from
as
on
at
use
it
we

by
cell
use
model
data
result
that
from
show
sampleshow
cellresult
from
model
data
study
value
increasesamplevalue
concentration
show
study
surfacedegreeparameter
observeanalysisincreasevalue
use
surfaceshow
study
parameter
degreeobservephasedecrease

vesicletheorem
spectralconductivity
computation
tensilefluorescencedropletincubateposteriorpepaccretion
aerodynamic
estrogenidealize
enumeration
catheterplanktonannihilation
globular

21
22
23
24
25
26
27
28
29
30

or
can
not
which
show
they
resultbetween
cell
study

study
value
temperatureatasconcentration
proteinincrease
surfacedegreebetweenhigheffectlowtemperatureeachsystemsurfacedegreeanalysis

phaseinobtaindecreaseexperiment
methodsolution
effectequationfunctionanalysisexperimentobtainequationsolutionincreasemethodfunctionacid
effectalgorithm
decomposition
shear
axial
exponential
iteration
calibration
amplitude
silicaelectrodemackerelrout
suctionmononuclear
unperturbed
mimic
knockout
differentiatedsurrogate
uppermost

31
32
33
34
35
36
37
38
39
40

also
may
model
time
data
all
than
high
many
increaseanalysiseffectobserveobtainlow
highbetweenmethodphase
parameter

concentration
methodproteinobtainfunction
table
observeprocess
test
rate

low
acid
sequence
determine
particlerespectively
interaction
highcorrespondindicatelowparticlerespectively
interaction
sequence
correspondexperimental
algorithm
determinedensitymodulation
soluteacetatediffusion
kinasephenotyperespiration
oscillation
latticeviscosity

dryness
prostate
ellipticalluminalwoody
crossoveraforementioned
dyslexia
quadrant
conceptually

41
42
43
44
45
46
47
48
49
50

will
value
much
system
other
but
each
effectonly
lowonexperiment
solutionfunctiondecreaseeachorequation
systemtable

also
present
numbersolution
level
than
phase
different
experiment
determinetable
reactioncomponent
experimental
genealgorithm
densitymeasurement
flow
test

in
component
genemeasurement
reactionlayer
indicate
ratio
flow
distribution

selectivity
activation
coefficient
reactivity
dispersion
precipitation
spectroscopytransverse
simulation
denotesilt
condense
postoperativeshale
keyholesimulator
laterallymeteorite
graft
diurnalhigh-performancebucklehyperplasia
caninepipetteinfestation
dialysisTable 2 A Comparison of the Top 50 Words for Each Measure
5.1.2 Top 500 Word Grade Level Comparisons
Next, in order for these word lists to be useful pedagogically, we wanted to determine
their proficiency levels. This was done by investigating at what U.S. grade level thesewords would be understood by native English speaking (NS) children. In 1981, Daleand O’Rourke published The Living Word Vocabulary which is “an inventory of the-8-written words known by children and young people in grades 4, 6, 8, 10, 12, 13, and 16”(1981: vii). Based on this data, Table 3 shows the average grade level at which NS
students would readily understand the central meaning of each word for the top 500
extractions produced by the statistical measures. To the best of our knowledge, there isno similar data available for grades one through three, so for this comparison, we usedreading grade word familiarity levels from Harris and Jacobson (1972). When we
calculated the average grades of all 500 words, any words not appearing in either
resource were labeled ‘17th grade’.
MeasuresAverage Grade

Freq
Dice
Cosine
CSM
LLR
Chi2
Yates
MI
McNemar

4.5
4.5
6.2
6.1
7.6
7.8
7.8
12.0
12.6

Table 3 U.S. Grade Level Based on Word Familiarity
In looking at the table, we can see that the top 500 words from Freq and Dice are
generally understood by fourth grade students on average; those of Cosine and CSM aregenerally understood by sixth grade students; those of LLR, Chi2 and Yates are
generally known by seventh grade students; those of MI and McNemar are generally
known by twelfth grade students.
In terms of practical pedagogical application, we inferred from this result, in
addition to several of our previous similar studies (Chujo and Utiyama, 2005; Chujo andUtiyama, 2006; Chujo, Utiyama and Oghigian, 2006; Chujo et al., 2007) that (1) the
EST words extracted by Freq, Dice, Cosine and CSM might be most useful for selecting
EST words for beginner level EST students; (2) the LLR, Chi2 and Yates lists might bemost useful for intermediate level EST learners; and (3) the MI and McNemar
vocabulary might be most appropriate for advanced level EST learners.
5.1.3 EST Dictionary Entry Word Overlap and Basic Vocabulary Overlap
Finally, to evaluate how effectively these tools extracted EST words, they werecompared to an existing EST vocabulary control list. For this we used the 5,640 entry
words in the EST Dictionary. In Figure 1, the black bar in the graph shows the overlap-9-in percentage between the EST dictionary entries and the top 500 words. We also
compared the top 500 words with the most often cited basic vocabulary, i.e. the GSL,
and this overlap is indicated with grey bars. The overlap with function words is shownwith white bars.
We can see the nine tools effectively produced relevant EST vocabulary. In
particular, we see that there is about a 67 percent overlap between the LLR, Chi2 andYates extractions and the dictionary entries, which are written for students at the junioror senior college level. Considering the conciseness and limited scale of the EST
dictionary entries we used in comparison with the divergent distribution of words in thetwenty-two domains of CPE master list, an overlap of 67 percent is reasonable5.
80
70
60
50
40
30
20
10
0

(%)
52.8

60.8

62.8

67.4

66.6

59.8

37.2

Freq
Dice

CosineFunction words

CS M

LLR

Chi2
Yates

Basic vocabulary

MI

McNemar

EST Dictionary

Figure 1 The Overlap of the Top 500 Extractions with the EST Dictionary Entries,
Basic Vocabulary, and Function Words
Let’s take a detailed look at the Chi2/Yates top 500 extractions, which show thehighest overlap (67.4 percent). In Table 4, some of the examples from the top 500
words are shown. Words that are in the EST dictionary are underlined, function words
are in parentheses, and words in the GSL are italicized6. Essential EST words included
in both the top 500 and the EST dictionary are words often used in ordinary scientificand technical subjects such as absorb, absorption, abstract, accumulation, acid,activation, activity, addition, adsorption, algorithm, allele, alloy, amino, amplitude,
analysis, angle, anion, anterior, antibody, antigen, application, approximation, aqueous,assay, atom, average, axial, and axis. On the other hand, examples not covered by the5

In a previous study, when we compared the equivalent top 500 extractions from the BNC
commerce component, which is less divergent than EST vocabulary, with business dictionary entries,
we obtained an approximate 80 percent overlap (Chujo et al., 2007).
6
Since the GSL is based on ‘word families’ units, and this study is based on ‘lemma’ units, there
might be some discrepancy in counting and comparing words.
- 10 -

concise dictionary entries are abundance, activate, additional, analytical, analyze,
approximate, approximately, associate, and assume. Some of these words arederivations of the above-mentioned words. Others, particularly verbs such as associate
and assume, are not included probably because of the size of the dictionary.
(above ), absorb, absorption, abstract, abundance, accumulation, acid, activate, activation,
activity, addition, additional, adsorb, adsorption, algorithm, allele, alloy, (also ), amino,
amplitude, analysis, analytical, analyze, (and ), angle , anion, anterior, antibody, antigen,application, approximate, approximately, approximation, aqueous, assay, associate, assume,
atom, average , axial, axis
bacterial, bacterium, band , bandwidth, base , baseline, (be ), beam , behavior, (between ),bind , biomass, blot, bond, (both ), boundary , buffer, (by )calculate , calculation, calibration, carbon, case , catalyst, cation, cavity, cell, cellular,
chain , channel, characteristic, characterize, chemical, clinical, cluster, coefficient, column,
combustion, compare , comparison, complex, component, composite, composition,
compound, compression, computation, compute, concentration, condition , conductivity,
configuration, consider , consist, consistent, constant, constraint, contain , content ,continuous, control , core, correlate, correlation, correspond, criterion, crystal, current ,curve , cycle, cytokine
data, decomposition, decrease , define, deformation, degradation, degree , demonstrate,
denote, density, dependence, deposition, depth, derivative, derive, describe , detect,detection, detector, determine , deviation, diameter, differ, difference , different , diffraction,diffusion, dimension, dispersion, displacement, distribution, domain, dose, droplet,(during ), dynamic(each ), effect , elastic , electrode, electron, element, embryo, emission, encode, energy,
enzyme, equation, equilibrium, error, estimate, estimation, evaluate, excitation, exhibit,
experiment , experimental , exposure, expression, extract

Table 4 An Excerpt of Chi2/Yates Top 500 Extractions
In this top 500 list, 4.4 percent are function words (shown in parentheses) and whenremoved from the above list, the remaining list seems to comprise essential basic EST
words. From this result, we might conclude that the tools extracted most commonlyused EST words as well as basic EST words also used in general domains. The Yates isonly one instance, however, and a similar breakdown of the extracted words wasobserved for the Cosine, CSM, and LLR extractions.
As for Freq/Dice, although basic EST words such as acid, activation, activity, age,algorithm, analysis, animal, antibody, application, area, atom, and average wereidentified, more function words such as a, about, above, after, all, along, also, although,among, and, another, any, as, and at, and more basic vocabulary such as account, add,age, allow, amount, animal, appear, apply, and average are also identified. As- 11 -

mentioned earlier, the goal in using these measures is to narrow the number ofcandidates for the EST word list. By using these extracted lists, users can easily
manually delete irrelevant words. Although Freq/Dice have extracted beginner levelproficiency EST words, removing basic vocabulary and function words might be time
consuming.
Looking at the right most bars in Figure 1, we can see MI includes very few basic
words and MI and McNemar include no function words. Bearing in mind the resultsfrom Tables 1 and 2, and Figure 1, MI seems appropriate for extracting higher
proficiency level EST words. A close examination of the top 50 McNemar words inTable 2 turns up some puzzling entries such as mackerel, unperturbed, knockout andwoody. This underscores the idea that the extracted lists are not meant to be definitive
and that educators can use these as a starting point for crafting lesson-specificvocabulary. It also shows the importance of identifying words by domain, which will be
discussed in the next section. We’ll need a more thorough investigation of these higher
or more advanced level extractions.
6. Conclusions and Pedagogical Applications
In this study, EST words were identified from the CPE using nine statistical measures.
The identified EST words are grouped into three proficiency levels by [U.S.] grade level
and were examined for overlap with EST dictionary entries. Users can further refine thecandidates in the extracted lists based on their own appropriate contexts and levels.
Further research is aimed at developing these specialized vocabularies into e-learningmaterials for vocabulary building. Once these types of EST lists are created and refined,
concordancing programs can be used either by educators or students to confirm themeaning of words and find real phrases containing the words. PERC is preparing torelease the CPE using an online multi-functional search service called the Shogakukan
Corpus Network, which is also adopted for the online BNC search service and theonline CobuildDirect search service developed by the Shogakukan multimedia
department. Once available, concordance lines and collocation tables can be obtained
easily and examples are shown in Figures 2 and 3.
One pedagogical application might be to create an intermediate EST vocabulary list
from the LLR or Chi2/Yates data, and an advanced level EST vocabulary list from the
MI data. Samples of words and phrases with Japanese translations for the intermediate
and advanced levels are shown in Tables 5 and 6, respectively. In this way, students arepresented with vocabulary in chunks, or partial contexts, rather than just having a list ofwords.
- 12 -

Figure 2 Concordance Lines Showing ‘Protein’

Figure 3 Collocation Table Showing ‘Protein’- 13 -

word

Phrase 1

Phrase 2

equation式

differential equations

微分方程式

an integral equation

積分方程式

gene

遺伝子

gene products

遺伝子産物

a gene family

遺伝子族

density密度

the energy density

エネルギー密度cell density

細胞集密度

layer

層

the surface layers of the
metal

金属の表層

the boundary layer

境界層

measurement

測定（値）

temperature measurements

温度測定

a direct measurement of thewinds

風の直接測定membrane膜

the cell membrane

細胞膜

the inner membrane

内膜

parameterパラメータ

parameter values

パラメータ値

model parameter

モデルパラメータ

particle

粒子

metal particles

金属粒子

the average particle size

平均粒度

proteinタンパク質

protein synthesis

タンパク質合成

protein structure

タンパク質構造ratio

比率a signal/noise ratio

信号対雑音比ｓex ratio at birth

誕生時の性比Table 5 An Example of Intermediate Level EST VocabularywordPhrase 1

Phrase 2

activation

活性化

ｔhe activation energy

活性化エネルギー

cell activation

細胞の活性化

algorithm

アルゴリズムan efficient algorithm forsolving the problem

問題解決の効率的なアルゴリズム

algorithm development

ｱﾙｺﾞﾘｽﾞﾑ開発

coefficient

係数

the correlation coefficient

相関係数

the coefficient of variation

変動係数

gel

ゲル

silica gel

シリカゲル

collagen gel

コラーゲンゲル

gradient

勾配，傾き

a temperature gradient

温度勾配a pressure gradient

圧力勾配

neuronニューロン

sensory neurons

感覚ニューロン

motor neurons

運動ニューロン

oxidation

酸化oxidation reactions

酸化反応

the oxidation level

酸化レベル

simulation

シミュレーション

simulation results

シミュレーション結果

computer simulation

計算機シミュレーション

substrate

基質

substrate binding

基質結合

substrate recognition

基質の認識

thermal

熱のthermal dynamics

熱力学

thermal energy

熱エネルギーTable 6 An Example of Advanced Level EST Vocabulary
Furthermore, it is possible to provide additional information such as in which domaina particular word most often appears (see Table 7), or to develop lists according to thedomain (Table 8). It is clear that there exist a number of potential pedagogicalapplications, which is important when we consider that there are many kinds of learners.
At the university level in Japan, the reality is that although students master a senior highschool vocabulary of approximately 2,000 to 3,000 words, many freshmen andsophomore students need to review and practice to consolidate their knowledge and to
bridge the gap between their understanding of these words and their successful usage ofthese words. Thus, some institutes may wish to include basic words with a gradualintroduction of domain-specific words, where others might find it more useful to- 14 -

provide a cross-section of domains. In any event, the CPE has shown itself to be aninvaluable resource, and the applications of various statistics as demonstrated in thisstudy provide a powerful tool for educators in mining the CPE for specialized
vocabulary. The addition of concordancing and collocation tools provides an important
context and allows learners to understand how these types of words appear in andfunction in the language.
Appearing Frequently in These Domains

Word
activation① Biology② Medicine③ Food Sciencealgorithm① Computer Science② Electrical & Electronic E.

③ Telecommunications

coefficient① Construction & Building T.

② Mathematics③ Civil Engineeringgel① Food Science② Biology③ Materials Science

gradient① Forestry② Oceanography③ Construction & Building T.

neuron① Medicine② Biology③ Agricultureoxidation① Chemistry② Metallurgy③ Earth Sciencesimulation① Forestry② Telecommunications③ Nuclear Science & T.

substrate① Materials Science② Electrical & Electronic E.

③ Food Sciencethermal① Materials Science② Nuclear Science & T.

③ Earth Science

Table 7 An Excerpt of Words and Domains
Biology

Electrical
Food Science
Engeneering

Forestry

Materials
Science

Medicine

Metallurgy

1

activation

approximation

amino

basal

axial

antigenalloy

2anterior

cavityethanolbiomass

compression

baselinebinary3

assay

excitation

extraction

estimation

conductivity

dilutedeformation

4

nucleotide

interact

fatty

flux

degradation

elevatediffusion5

cleavageneurongel

gradientdyeinhibitor

displacement

6

hybridization

propagation

glucosenutrient

meshmediatefracture

7

incubatequantum

lipid

physiologicalmodulusneurongeometry8

induction

sensormetabolism

regression

radiusparasitelattice9intracellular

spectralpeptidesimulation

shear

subunitsimulate

10

peptidewavelength

purifyuptakesubstratevaccinetransient

Table 8 An Excerpt of Domain Specific Words- 15 -

For more information on the various statistics used in this study, or for word lists
created in previous studies, please see http://www5d.biglobe.ne.jp/~chujo/ and/or
http://www2.nict.go.jp/jt/a132/members/mutiyama. For more information on
Shogakukan, Inc., please see http://www.corpora.jp/.
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