Selecting Level-Specific Kyoto Tourism
Vocabulary Using Statistical Measures
Kiyomi Chujo
Nihon University
chujo@cit.nihon-u.ac.jp

Masao Utiyama
Kathryn Oghigian
NICTTokyo International University
mutiyama@nict.go.jp
oghigian@gmail.com

The Japanese government’s “Action Plan for Tourism Development” in 2003
has prompted colleges and universities to set up departments to specialize in tourism. In
order to supply educators with keywords associated with tourism, this study selected
beginner, intermediate and advanced level specialized vocabulary using statistical tools
previously established to identify level-specific, domain-specific words (Chujo andUtiyama, 2005, 2006). In this study, a Kyoto tourism corpus was compiled from ‘Kyotoguide’ texts that consists of four components: ‘miru’ (sight-seeing), ‘kau’ (shopping),‘taberu’ (dining), and ‘taikensuru’ (hands-on activities). The corpus was then comparedwith the British National Corpus High Frequency Word List (Chujo, 2004) using
statistical measures such as the log likelihood ratio and mutual information. An
examination of the resulting vocabulary lists showed that each statistical measureextracted an appropriate level of domain-specific words by its vocabulary level, gradelevel, and school textbook vocabulary coverage.

BACKGROUND
According to the Japan National Tourist Organization, the total number ofJapanese tourists abroad in 2005 reached 17.4 million, while the total number ofinternational visitors to Japan was estimated to be 6.7 million1. This imbalance between
outbound and inbound tourism was the impetus behind the Japanese government’s 2003
“Action Plan for Tourism Development2.” Measures such as the ‘Visit Japan Campaign’
have been implemented to focus on significantly increasing inbound tourism and havebeen giving a considerable boost to Japan’s recent tourism development.
In response, many colleges and universities3 have set up faculties and departmentsthat specialize in tourism and its corresponding human resource development. One of thefundamental academic subjects taught is English for Tourism, an English for occupational
purposes (EOP) course of study which is one of many types of English for specificpurposes (ESP) (Robinson, 1991). One of the prominent characteristics of ESP is a heavy
load of corresponding specialized vocabulary or “technical words that are recognizably
specific to a particular topic, field, or discipline” (Nation, 2001:198). Since vocabulary
expansion is essential for ESL and EFL learners to gain proficiency in English (Nation,1994), it follows that tourism vocabulary would be essential to any academic tourism
program.

REVIEW OF LITERATURE
Several subdivisions exist under the broad umbrella of “tourism English”:
language and communication for hotels, restaurants and catering, transportation, tours,
ticketing and itineraries, resort facilities, and various support retail services as well as
handling money, giving or dealing with complaints, health and safety issues, eco-tourism,
business, marketing and accounting issues, etc. Even within these subdivisions there arefurther divisions, for example, a person in a hotel management position may have a
different subset of vocabulary and phrases than a bell hop or a housekeeper; similarly the
person handling ticketing at a travel agency may not necessarily also be doing marketingor accounting.
There are course books and resources available on tourism English, and some are
more comprehensive than others. Wood’s (2003) Tourism and Catering covers a wide
range of aspects, as does Check Your English Vocabulary for Leisure, Travel, and Tourism(Wyatt, 2006). Resources that cast a net over a wider area tend not to be ascomprehensive as those focused on a narrow subset, and those that are morecomprehensive tend to focus only on a limited area. A good example of the latter is Readyto Order (Baude, Iglesias and Inesta, 2006), which provides in-depth language for chefs,
bartenders and wait staff. So while tourism resources do exist, many seem to offer either
a superficial view of many areas, or an in-depth look at one area. To the best of ourknowledge, there is no definitive tourism resource that provides in-depth coverage for all
aspects of tourism.
In addition, with regard to those resources that do provide more in-depth language,
Walker (1995) reports that these have limited value because “a great deal of what iscurrently available (English for Hotel Staff, Nelson; May I help you? Cassell; etc.) is too
job-specific for the requirements of those following courses in Travel and Tourism at
Diploma or Degree levels, since many such students are often uncertain as to which ofeven the major divisions of tourism attracts them most.” Given the inevitable nature of
students whose target situations are still largely undefined, and the somewhat hit-or-missresources currently available, it is apparent that a more comprehensive tourism
vocabulary list applicable to wider divisions in tourism may be a useful resource.
PURPOSE OF THE STUDY
The goal of this study is to provide a more comprehensive, broader-based tourism
lexicon for Japanese educators and students. This was done by first determining whatmight be the most meaningful vocabulary based on research on popular Japanesedestinations and activities, identifying an appropriate corpus, and then extracting various
levels of tourism words by applying statistical measures to the corpus. Once identified,vocabulary level, grade level, and Japanese high school textbook coverage were
investigated, resulting in the creation of beginner, intermediate and advanced leveltourism vocabulary.
PROCEDURE
Corpus and Methodology

In order to determine how to target the most meaningful vocabulary, we
researched statistics on inbound visitors’ destinations and preferred activities in Japan.
The most frequently visited prefectures by foreign visitors were Tokyo, Osaka, Kyoto,
Kanagawa, and Chiba (Mukaiyama, 2003; METI Kansai, 2004; Kamio, 2005). Favored
activities were experiencing the ‘two-sides of Japan’: modern Japan’s culture andlifestyle (sightseeing in large cities, shopping and visiting fashionable areas) and its
traditional culture (dining on traditional dishes and visiting places of scenic beauty andhistoric interest) (Kamio, 2005). We also studied the “Best 100” plans published by the
Agency of Cultural Affairs (2005) and among these, the most preferred prefectures forJapanese travelers were Kyoto, Nara, and Tokyo. In addition, it was reported in a recent
academic survey that the city that Japanese college students would most like to introduce
to visitors from overseas was Kyoto, followed by Tokyo (Ichimura, 2004).
It was fortuitous that Kyoto was named as a highly ranked destination because
one of the researchers in this study was previously involved in a project related to theabove-mentioned ‘Visit Japan Campaign’ and developed a Kyoto-guide corpus in English.
This Kyoto tourism data covers various aspects of modern and traditional Japan,
including its history, culture, current events, and local tourist attractions. This corpus
provides specialized vocabulary for both a highly ranked destination and a broad range ofactivities popular with tourists, and could be applicable as a broad-based database fortourism students as well as general English learners who want to be able to discuss Japanand Japanese culture in English (Dantsuji, 2001).
Lam (2004) reminds us that tourism English is very different from general
English and that priority should be given to teaching the use of keywords. However,separating technical vocabulary (in this case tourism vocabulary) from general
vocabulary has not been an easy task (Briggs and Lee, 2002) since this is time-consuming
and heavily dependent on the selector’s expertise in English education and specialist
knowledge of the field (Utiyama et al., 2004). Chujo and Utiyama (2004) and Utiyama etal. (2004) have established an easy-to-use tool employing various statistical measures to
identify level-specific, domain-specific words. Chujo and Utiyama (2005) created a listof written science vocabulary by applying those nine statistical measures to the 7.37million-word written ‘applied science’ component of the British National Corpus (BNC).
They found that each measure extracted a different level of domain-specific words by
vocabulary level, grade level, and school textbook vocabulary coverage and that specificmeasures produced level-specific words, for example, the log likelihood ratio (LLR)
identified intermediate-level technical words, and mutual information (MI) identified
advanced level technical words. These measures were effective in separating technicalvocabulary from general-purpose vocabulary, and provide a useful template as a means ofidentifying domain-specific vocabulary. Thus the Kyoto corpus was identified as ourtarget database, and the statistical measures as our methodology.
Kyoto Tourism Word List
The Kyoto tourism corpus includes 885 Kyoto guide texts in four subcategories:(1) 160 ‘miru’ (sight-seeing) texts, (2) 317 ‘kau’ (shopping) texts, (3) 345 ‘taberu’(dining) texts, and (4) 63 ‘taikensuru’ (hands-on activities) texts (see Table 1). Each text
is about 47 words long on average and describes some aspect of tourism related to Kyoto,

for example: the history of a shrine, the best place to shop for a certain item, specialties
of a restaurant, or a description of a hands-on pottery class. All the words in this corpus
were first lemmatized to extract all the base forms using the CLAWS7 tag set4. (Forexample, eat, eats, ate, eating, and eaten are all forms of a single lemma and were listed
under a base word eat with a frequency of five occurrences.) Secondly, all proper nouns
and numerals were identified by their part of speech tags and deleted manually. This
yielded a 2,786-word Kyoto tourism master list.
Table 1 Composition of the Kyoto-Guide Corpus

Miru (Sight-seeing)
Kau (Shopping)
Taberu (Dining)
Taikensuru (Hands-on)
Total corpus

Number of texts
160
317
345
63
885

Types
1,470
1,553
1,463
653
2,786

Tokens
9,236
13,64916,1752,965
42,025Three Control Lists
Three control lists were used for creating the extracted Kyoto tourism vocabulary
and for investigating the vocabulary level, grade level, and school textbook vocabulary
coverage of the statistically extracted vocabulary. These control lists were created using
the same lemmatizing procedures described above.
(1) The British National Corpus High Frequency Word List (BNC HFWL) is a list of
13,994 lemmatized words representing 86 million BNC words that occur 100 times ormore. (The compiling procedure is detailed in Chujo, 2004.) The British National Corpus(BNC) represents 100 million words of spoken and written British English. By
comparing the tourism words in our master list to the BNC HFWL, we can statisticallydetermine how they would appear differently from words in a general corpus.
(2) The Living Word Vocabulary (Dale and O’Rourke, 1981) includes more than 44,000
items, and each has a percentage score that rate whether the word is familiar to students
in U.S. grade levels 4 through 16. For supplementing grade levels 1 through 3, reading
grades from Basic Elementary Reading Vocabularies (Harris and Jacobson, 1972) wereused. By comparing the tourism words in our master list to this list, we can determine thegrade level at which the central meaning of a word can be readily understood.
(3) The junior and senior high school (JSH) textbook vocabulary list containing 3,245
different base words was compiled from the top selling series of Japanese high school
textbooks (the New Horizon 1, 2, 3 series and the Unicorn I, II and Reading series) inJapan. Japanese high school students generally use these or similar books to study
English before entering a university. By comparing the tourism words in our master list to
this list, we can determine which words have already been studied by most Japanese highschool graduates.
Statistical Measures Used to Identify Outstanding Tourism Words
To extract level-specific vocabulary from the Kyoto tourism corpus, we used fivestatistical measures: simple frequency (Freq), the complementary similarity measure(CSM), the log likelihood ratio (LLR), the chi-square test with Yates’s correction (Yates),and mutual information (MI)5. The formula for each measure is available on the web6. A
detailed description of each measure can be found in Utiyama et al. (2004) and Chujo andUtiyama (forthcoming 2006), and the notation for these kinds of statistics can be found in
Scott (1997).
These statistical measures are widely used in computational linguistics. They
automatically identify outstanding words in frequency of occurrence by making
comparisons between one specified list (in this case, the tourism words in our master list)
and another larger list (the BNC HFWL). Thus when each statistical measure is applied tothe tourism master list, these statistics indicate whether a word is overused or underused
compared with a list of general English. Applying each statistical score results in a list of
extracted words. The statistical score for the extent of each word’s “outstanding-ness”(Scott, 1999) in frequency of occurrence is computed, and the words are sorted from themost outstanding to the least outstanding. Thus the words near the top are ranked mostoutstanding in terms of each statistical measure’s criteria. The goal in using thesemeasures is to narrow down the number of candidates from the 2,786 different words inthe tourism master list into more manageable sub-lists, but these sub-lists are not meant
to be definitive. These statistical tools can help users to select technical vocabulary
automatically without specialist knowledge, and by using these extracted lists, users caneasily manually delete irrelevant words.
Verifying the Vocabulary Levels of the Extracted Lists
Each of the five measures produced a sub-list. All the extracted lists were initially
examined for an overview comparison of the top 20 extracted words in order to get anindication of the general tendencies for each list. Next, the 300 most outstanding words ofeach list were examined for their potential for pedagogic applications by comparing themto the three control lists: (1) the BNC HFWL to understand how frequently these words
are used compared to general English usage (frequency distribution); (2) the Living Word
Vocabulary and Basic Reading Vocabularies, to determine word familiarity based on
grade level, and (3) the JSH vocabulary list to investigate the number of words coveredby the JSH textbook vocabulary. Based on this information, each extracted list was
designated as beginner, intermediate, or advanced. This is detailed in the next section.
RESULTS AND DISCUSSIONSTop 20 Extracted Words Overview Comparison
The top 20 words from each of the five measures in descending order are shownin Table 2. The bottom two rows of each column show the average frequency score andaverage word length of the top 20 words extracted by each statistical measure. The lists inTable 2 are very different from each other even though they were extracted from thesame tourism master list. A glance at the top 20 words shows the general tendencies
inherent in the extraction of these five measures.
The top 20 words identified by Freq include general words such as the, be, and of,which usually appear at the top of high frequency word lists. From CSM to MI, the top 20
extractions appear to include tourism words that gradually shift from simple and general
to complex and specific. For example, we can recognize dining words such as restaurant,
dish, sweet, and tea in the top 20 CSM words, while we can see specialty, bakery,
homemade, confectionery, paste, leek, mackerel, dish, and delicacy in the top 20 MI list.
This step verifies that the measures do in fact extract different vocabulary from the same
master list.
As we see from the data in the bottom two rows of Table 2, the average frequency
score of each list decreases from left to right or from Freq to MI. Inversely, the averageword length increases from left to right, ranging from 3.3 to 7.7 letters. As Chujo,
Utiyama, and Nishigaki (forthcoming 2006) have shown, difficulty levels increase withincreasing word length. Although we are aware that word difficulty may be influenced bymany more factors than frequency and word length, this might support the possibility thatspecific statistical measures can be used to target specific grade level vocabulary. This
will be explored in the following sections.
Table 2 A Comparison of the Extracted Tourism Word Lists
Rank

Freq

CSM

LLR

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

the
be
of
and
a
in
to
this
shop
it
restaurant
have
for
can
with
as
by
that
dish
guest

shop
this
restaurant
dish
guest
serve
in
sell
sweet
enjoy
use
item
visitorfeatureoftea
templevariouscanproducerestaurant
shop
dish
guest
sweet
templeserve
visitorsell
ingredientenjoy
flavor
item
tea
rice
locateatmospherefeaturebakery
cake

Yates

MI

restaurant
dishbakeryshop
specialty
temple
guest
sweet
paste
ingredientflavorlong-establishedhomemadericeseasonal
well-established
precinctwaredyelocatespecialty
long-establishedbakeryhomemadeprecinctconfectionery
paste
well-establishedlacquerblossomsundrybamboo
ware
leek
dye
ceramic
mackereldishdelicacyseasonal

Average Frequency

792.4

396.7

213.9

152.1

53.1

Average Word Length

3.3

4.9

5.8

7.2

7.7

Top 300 Word Frequency Distribution Comparisons
We next examined the frequency distribution of the top 300 extracted words byusing the BNC HFWL, which was divided into fourteen 1000-word frequency bands7.
‘BNC frequency band 1000’ indicates words ranked from 1 to 1000, meaning the words
appearing in these bands are the 1st to 1000th most frequently appearing words in
English. ‘Frequency band 2000’ corresponds to a ranking of 1001 to 2000, etc. The
percentages of the 300 words of each list that belong to each frequency band are shown in
Table 3. Highlighted scores show the BNC frequency bands at which the top 60% of theextracted words belong8.

We can see the clear graduations of frequency levels. Looking across the tablefrom Freq to MI, the top 300 words belong to increasingly lower frequency bands. About
60% of the top 300 words from Freq belong to the top 1000 BNC band and about 60% of
the CSM words belong to the top 2000 BNC words. About 60% of the LLR and Yateswords belong to the top 5000 or the top 6000 BNC bands. Uniquely, MI extracts words
evenly from all the frequency bands of BNC HFWL words and about 60% belong to the
top 9000 BNC words. This table provides a graphic illustration of how frequently or
infrequently these extracted words are used in English. We can also see that the fivestatistical measures clearly extract different outstanding levels of tourism words.
Table 3 Frequency Distribution of the Top 300 Extractions
BNC
Frequency
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,00011,00012,00013,00013,994

Freq

CSM

LLR

Yates

MI

60.7
14.0
7.3
4.7
2.7
2.0
1.3
1.7
2.3
1.0
1.0
0.7
0.7
0.0

39.0
20.3
10.3
8.0
4.7
3.0
2.3
2.7
3.3
2.7
1.7
0.7
0.7
0.7

22.3
17.0
10.7
9.3
7.7
5.7
5.7
3.3
6.0
4.7
2.0
2.3
1.3
2.0

16.0
13.3
10.0
8.7
8.0
6.0
6.7
5.0
7.0
6.3
4.0
3.7
2.0
3.3

4.3
4.3
6.0
7.3
7.7
7.7
7.3
8.0
10.7
9.3
7.7
5.0
6.3
8.3

Top 300 Word Grade Level Comparisons
Next we investigated at what US grade level the top 300 words would beunderstood by native English speaking children. The Living Word Vocabulary (Dale andO’Rourke, 1981:vii) is “an inventory of the written words known by children and young
people in grades 4, 6, 8, 10, 12, 13, and 16.” To supplement grades 1 through 3, we usedreading grade word familiarity levels from Harris and Jacobson (1972). Figure 1 shows
at what grade level the majority of native speaking students (90%) would readily
understand the central meaning of each word for the top 300 extractions produced by thestatistical measures. Note that in Figure 1, ‘N/A’ denotes those words not appearing ineither of the two resources.
In looking at the horizontal line and corresponding grade levels for each bar in thegraph, we can see that 90% of the top 300 words from Freq are understood by 6th gradelevel students; those of CSM are understood by 8th grade students; those of LLR and
Yates are known by 10th and 12th grade students respectively; those of MI are known by
13th grade students (college freshmen). It is interesting that similar results were obtainedin a study using these measures to extract science vocabulary from a BNC science
component (see Chujo and Utiyama, 2005), and these similar results support theeffectiveness of using these measures to extract domain and level specific vocabulary.

100%
N/A
6

8

10

12

13

80%

16
13
12

60%

10
8
6

40%

4
3

20%

2
1

0%
Freq

CSM

LLR

Yates

MI

Figure 1 Word Familiarity Based on US Grade Level
Japanese High School Textbook Vocabulary Coverage
As educators in Japan, our primary interest is in how this extracted tourism
vocabulary compares to what Japanese students may or may not already have studied andtherefore how useful these lists might be. To do this, we compared the top 300 extractions
to the vocabulary representing what most high school students have studied before
entering university. This list, comprised of 3,245 different words, was compiled from the
top selling series of junior and senior high school (JSH) textbooks in Japan from the 7th
through 12th grades. Figure 2 shows both what percentage of the top 300 extractions do
appear in JSH textbooks in the lower section of the graph, and what percentage do notappear, in the upper section of the graph. We see from Figure 2 that 82 percent of the
Freq top 300 extractions are covered in the JSH school textbooks; 70 percent of the CSM
extractions; 57 percent of the LLR, 50 percent of the Yates, and 32 percent of the MI
extractions are covered in the JSH school textbooks. The data in Figure 2 again verifies
that the five different statistical measures extract quite different levels of words andprovides us important information on which of the top 300 words might be appropriate
for learners.
100%
18
30

80%

43

50
68

60%

40%

82
70
57

50

20%

32

0%
Freq

CSM
Covered by JSH

LLR

Yates

MI

not Covered by JSH

Figure 2 Percentage of Top 300 Words Covered/Not Covered by the JSH Textbook

Developing Level-Specific Tourism Lists
The results of these analyses support the finding that certain statistical measurestend to extract tourism vocabulary corresponding to a certain grade level. In terms ofpractical pedagogical application, we inferred from frequency distribution, grade levelfamiliarity data, and JSH text coverage data in this current study, in addition to several
similar previous studies (Chujo and Utiyama, 2004; 2005; 2006), that (1) the tourism
words extracted by Freq and CSM might be most useful for beginner level Japanese EFL
learners; (2) the LLR and Yates lists might be most useful for intermediate level Japanese
EFL learners; and (3) the MI vocabulary might be most appropriate for advanced level
Japanese EFL learners.
In order to aid retention, it would be more convenient for educators if the
vocabulary were classified according to subtopics. The original Kyoto tourism corpus
consists of four components relating to the target activities: ‘miru’ (sight-seeing), ‘kau’(shopping), ‘taberu’ (dining), and ‘taikensuru’ (hands-on activities). We also developed
similar level-specific tourism lists from these components using the same five statisticalmeasures and created 20 sub-lists (5 measures x 4 components), whose vocabulary levels
were similarly verified. The top 50 words from the advanced MI list are shown in Table 4.
In Table 4, we can easily visualize a wide range of tourism topics for the four
sub-sections. For example, looking at the ‘miru’ list, we can enjoy seasonal changes oflandscapes, particularly cherry blossoms in spring, lush greenery in early summer andcrimson maple foliage in autumn while strolling temples and worshiping deities at shrines.
In looking at the ‘taberu’ section, our appetite is stimulated by delicacies which embody
the traditional culinary culture of Kyoto, and the authentic Kyoto-style dishes featuring
tofu, seasonal ingredients, mackerel sushi, and homemade soybean paste served at longestablished restaurants. Keep in mind that these are only 50 of the top 300 words for eachlist (beginner and intermediate are not shown), and that the list provides a manageable
resource which can be further refined by educators and students.

CONCLUSION
In this study, tourism words were selected from a Kyoto tourism corpus using fivestatistical measures. An examination of the resulting vocabulary lists show that eachstatistical measure extracted an appropriate level of tourism words by its vocabulary level,
grade level, and school textbook vocabulary coverage. They were classified into three
proficiency level lists, which allow users to further refine the candidates based on theirown appropriate contexts and level. Further research will include developing these lists
into e-learning materials for vocabulary building.
Although the vocabulary lists produced by this study do not provide a definitive,
comprehensive lexicon for all the sub-divisions in tourism (hotel, restaurant, tours,
marketing, etc.), it has been possible to create level-specific, broad-based vocabulary lists
not only useful for university teachers and students majoring tourism, but also useful for
general students who are interested in developing skills to express Japan’s culture in
English. Since a Kyoto corpus was used, many of these key words are common to Japan’s
traditional culture and are therefore particularly applicable to tourism in Japan.

Table 4 Advanced Level Domain-Specific Tourism Words
miru

kau

taberutaikensuru

precinct
blossomshrinetemplemaple
greenerydeity
foliagestrollenshrine
worshipper
scenicreposerelocate
crimsonsundrypanoramic
popularly
confectionery
bamboospacious
thrilling
erect
inscribe
open-aircherryvisitormoss
bloom
ceramic
entertainer
extantfamebirthplace
promenadesecludepavilioncrystallization
tranquil
curd
gourmetbackdrop
potterobservatory
boutiquepastimeseasonal
masterpiecerustleannex

bakeryspecialty
long-establishedlacquersundryconfectionery
bamboowell-established
ware
paste
ceramic
incensefootwearsecondhand
homemade
accessory
fragrance
sweetnesshandmadehigh-quality
adjoiningcherishsash
flavorindigomanuallysoyentertainer
fluffydye
additive
sweet
souvenirseasonal
teatimeglutenboutiquegrill
showcase
ingredient
comb
bread
yeast
artisanfound
affordable
shop
rice
brackencosmetic

homemade
specialty
long-establishedleekmackereldishrestaurant
paste
delicacy
stew
seafoodcafewell-establishedfluffyculinaryseasonalauthentic
ingredientconfectionery
entertainer
curry
rice
grill
guest
chef
blossom
flavorsweetnessfragrance
glutenteatimedessertmanuallyeelpastimeaffordable
ediblesauce
curd
gourmetbrothvegetarian
meticulous
soycherishprecinct
octopusabundant
locatecrystallizationdyepotterware
ceramic
cedar
lacquer
meditationsash
indigoengravingprecincttempleincensesermonbambookilnartisanunparalleledblossomaromatic
napkinvisitorconfectionery
mementobrightenwell-establishedhandkerchiefaffordable
doll
wholesaler
alcoveburnerhandmadebraceletgoblinadjoinsouvenir
sect
vase
foil
purification
reservation
seedling
beginnerparaffinregrettableenshrine
culinary
manually
accessory

Notes
1 Tourism statistics: http://www.tourism.jp/english/
2 Action Plan for Tourism Development:
http://www.kantei.go.jp/jp/singi/kanko2/kettei/030731/keikaku.pdf
3 As of 2005, there are 25 universities that have departments of tourism in Japan.

4 Claws7: http://www.comp.lancs.ac.uk/computing/users/eiamjw/claws/claws7.html.
5 References for each measure are as follows: CSM (Wakaki and Hagita, 1996), LLR(Dunning, 1993), Yates (Hisamitsu and Niwa, 2001), and MI (Church and Hanks,
1989).
6 The formulas are available at http://www2.nict.go.jp/jt/a132/members/mutiyama.
7 From our previous studies, we can assume that the top 300 words (about 10% of themaster word list) provide a clear illustration of each measure’s unique extractions. Fora more detailed discussion, see Utiyama, et al. (2004).
8 The ‘60%’ level was chosen arbitrarily in order to easily observe the graduations offrequency levels. An ‘80%’ level will provide a similar graphic illustration as shown in
Table 3.
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