Segmentation and Translation of Japanese Multi-word
Loanwords
James Breen
Timothy Baldwin
The University of MelbourneThe University of Melbourne
jimbreen@gmail.com
tb@ldwin.net

Abstract
The Japanese language has absorbed
large numbers of loanwords from many
languages, in particular English. As
well as using single loanwords, compound nouns, multiword expressions(MWEs), etc. constructed from loanwords can be found in use in very large
quantities. In this paper we describea system which has been developed
to segment Japanese loanword MWEs
and construct likely English translations. The system, which leverages theavailability of large bilingual dictionaries of loanwords and English n-gram corpora, achieves high levels of accuracy indiscriminating between single loanwords
and MWEs, and in segmenting MWEs.
It also generates useful translations ofMWEs, and has the potential to being amajor aid to lexicographers in this area.

1

Introduction

The work described in this paper is part ofa broader project to identify unrecorded lexemes, including neologisms, in Japanese corpora. Since such lexemes include the range oflexical units capable of inclusion in Japanesemonolingual and bilingual dictionaries, it isimportant to be able to identify and extracta range of such units, including compoundnouns, collocations and other multiword expressions (MWEs: Sag et al. (2002), Baldwin
and Kim (2009)).
Unlike some languages, where there is ofﬁcial opposition to the incorporation of foreign words, Japanese has assimilated a large
number of such words, to the extent that

Francis Bond
Nanyang Technological
University, Singapore
bond@ieee.org

they constitute a sizeable proportion of thelexicon. For example, over 10% of the entries and sub-entries in the major Kenkyūsha
New Japanese-English Dictionary (5th ed.)(Toshiro et al., 2003) are wholly or partly
made up of loanwords. In addition there areseveral published dictionaries consisting solelyof such loanwords. Estimates of the numberof loanwords and particularly MWEs incorporating loanwords in Japanese range into thehundreds of thousands. While a considerable
number of loanwords have been taken from
Portuguese, Dutch, French, etc., the overwhelming majority are from English.
Loanwords are taken into Japanese byadapting the source language pronunciation
to conform to the relatively restricted set ofsyllabic phonemes used in Japanese. Thus
“blog” becomes burogu, and “elastic” becomeserasutikku. When written, the syllables of theloanword are transcribed in the katakana syllabic script (ブログ, エラスティック), which
in modern Japanese is primarily used for thispurpose. This use of a speciﬁc script means
possible loanwords are generally readily identiﬁable in text and can be extracted withoutcomplex morphological analysis.
The focus of this study is on multiword
loanwords. This is because there are now large
collections of basic Japanese loanwords alongwith their translations, and it appears thatmany new loanwords are formed by adopting
or assembling MWEs using known loanwords.
As evidence of this, we can cite the numbers of katakana sequences in the the Google
Japanese n-gram corpus (Kudo and Kazawa,
2007). Of the 2.6 million 1-grams in that cor-pus, approximately 1.6 million are in katakana
or other characters used in loanwords.1 Inspection of those 1-grams indicates that once
the words that are in available dictionaries
are removed, the majority of the more common members are MWEs which had not been
segmented during the generation of the corpus. Moreover the n-gram corpus also contains 2.6 million 2-grams and 900,000 3-grams
written in katakana. Even after allowing forthe multiple-counting between the 1, 2 and 3grams, and the imperfections in the segmentation of the katakana sequences, it is clear thatthe vast numbers of multiword loanwords inuse are a fruitful area for investigation with aview to extraction and translation.
In the work presented in this paper we describe a system which has been developed tosegment Japanese loanword MWEs and construct likely English translations, with the ultimate aim of being part of a toolkit to aid
the lexicographer. The system builds on theavailability of large collections of translated
loanwords and a large English n-gram corpus,
and in testing is performing with high levelsof precision and recall.

2

Prior Work

There has not been a large amount ofwork published on the automatic and semiautomatic extraction and translation ofJapanese loanwords. Much that has beenreported has been in areas such as backtransliteration (Matsuo et al., 1996; Knightand Graehl, 1998; Bilac and Tanaka, 2004),
or on extraction from parallel bilingual corpora (Brill et al., 2001). More recently work
has been carried out exploring combinationsof dictionaries and corpora (Nakazawa et al.,2005), although this lead does not seem tohave been followed further.
Both Bilac and Tanaka (2004) andNakazawa et al. (2005) address the issue ofsegmentation of MWEs. This is discussed in
3.1 below.
1 In

addition to katakana, loanwords use the ー(chōoN) character for indicating lengthened vowels,and on rare occasions the ヽ and ヾ syllable repetition characters.

3 Role and Nature of Katakana
Words in Japanese
As mentioned above, loan words in Japaneseare currently written in the katakana script.
This is an orthographical convention that has
been applied relatively strictly since the late
1940s, when major script reforms were carriedout. Prior to then loanwords were also written
using the hiragana syllabary and on occasions
kanji (Chinese characters).
The katakana script is not used exclusively
for loanwords. Other usage includes:a. transcription of foreign person and place
names and other named entities. ManyJapanese companies use names which
are transcribed in katakana. Chinese(and Korean) place names and personnames, although they are usually available in kanji are often written in katakanatransliterations;b. the scientiﬁc names of plants, animals,etc.
c. onomatopoeic words and expressions, although these are often also written in hiragana;d. occasionally for emphasis and in somecontexts for slang words, in a similar
fashion to the use of italics in English.
The proportion of katakana words that were
not loanwords was measured by Brill et al.
(2001) at about 13%. (The impact and handling of these is discussed brieﬂy at the end of
Section 4.)
When considering the extraction ofJapanese loan words from text, there are anumber of issues which need to be addressed.
3.1 Segmentation
As mentioned above, many loanwords appear in the form of MWEs, and their correct
analysis and handling often requires separation into their composite words. In Japanese
there is a convention that loanword MWEs
have a “middle-dot” punctuation character(・) inserted between the components, however while this convention is usually followed in dictionaries, it is rarely applied elsewhere. Web search engines typically ignore
this character when indexing, and a search
for a very common MWE: トマトソース

tomatosōsu “tomato sauce”, reveals that it almost always appears as an undiﬀerentiatedstring. Moreover, the situation is confused by
the common use of the ・ character to separate items in lists, in a manner similar to asemi-colon in English. In practical terms, systems dealing with loanwords MWEs must be
prepared to do their own segmentation.
One approach to segmentation is to utilize a Japanese morphological analysis system.
These have traditionally been weak in the areaof segmentation of loanwords, and tend todefault to treating long katakana strings as
1-grams. In testing a list of loanwords and
MWEs using the ChaSen system (Matsumoto
et al., 2003), Bilac and Tanaka (2004) report a
precision and recall of approximately 0.65 on
the segmentation, with a tendency to undersegment being the main problem. Nakazawa
et al. (2005) report a similar tendency with
the JUMAN morphological analyzer (Kurohashi and Nagao, 1998). The problem wasmost likely due to the relatively poor representation of loanwords in the morpheme lexicons used by these systems. For example the
IPADIC lexicon (Asahara and Matsumoto,2003) used at that time only had about 20,000
words in katakana, and many of those were
proper nouns.
In this study, we use the MeCab morphological analyzer (Kudo et al., 2004) with therecently-developed UniDic lexicon (Den et al.,2007), as discussed below.
As they were largely dealing with nonlexicalized words, Bilac and Tanaka (2004)
used a dynamic programming model trainedon a relatively small (13,000) list of katakana
words, and reported a high precision in theirsegmentation. Nakazawa et al. (2005) useda larger lexicon in combination with the JUMAN analyzer and reported a similar highprecision.
3.2

Non-English Words

A number of loanwords are taken from languages other than English.
The JMdict
dictionary (Breen, 2004) has approximately
44,000 loanwords, of which 4% are marked as
coming from other languages. Inspection ofa sample of the 22,000 entries in the Gakken
A Dictionary of Katakana Words (Kabasawa

and Satō, 2003) indicates a similar proportion. (In both dictionaries loanwords fromlanguages other than English are marked with
their source language.) This relatively small
number is known to cause some problems
with generating translations through transliterations based on English, but the overall impact is not very signiﬁcant.
3.3 Pseudo-English Constructions
A number of katakana MWEs are constructions of two or more English words forming aterm which does not occur in English. An example is バージョンアップ bājoNappu “version up”, meaning upgrading software, etc.
These constructions are known in Japanese
as 和製英語 wasei eigo “Japanese-made English”. Inspection of the JMdict and Gakken
dictionaries indicate they make up approximately 2% of katakana terms, and while a
nuisance, are not considered to be a signiﬁcant problem.
3.4 Orthographical Variants
Written Japanese has a relatively high incidence of multiple surface forms of words,
and this particularly applies to loan words.
Many result from diﬀerent interpretationsof the pronunciation of the source languageterm, e.g. the word for “diamond” is both
ダイヤモンド daiyamoNdo and ダイアモンド
daiamoNdo, with the two occurring in approximately equal proportions. (The JMdict dictionary records 10 variants for theword “vibraphone”, and 9 each for “whiskey”
and “vodka”.) In some cases two diﬀerent
words have been formed from the one source
word, e.g. the English word “truck” was borrowed twice to form トラック torakku meaning “truck, lorry” and トロッコ torokku meaning “trolley, rail car”. Having reasonably complete coverage of alternative surface forms isimportant in the present project.

4 Approach to Segmentation and
MWE Translation
As our goal is the extraction and translation of loanword MWEs, we need to address
the twin tasks of segmentation of the MWEs
into their constituent source-language components, and generation of appropriate transla-

tions for the MWEs as a whole. While theback-transliteration approaches in previous
studies have been quite successful, and havean important role in handling single-wordloanwords, we decided to experiment with analternative approach which builds on the large
lexicon and n-gram corpus resources which
are now available. This approach, which wehave labelled “CLST” (Corpus-based Loanword Segmentation and Translation) builds
upon a direction suggested in Nakazawa et al.
(2005) in that it uses a large English n-gram
corpus both to validate alternative segmentations and select candidate translations.
The three key resources used in CLST are:a. a dictionary of katakana words which has
been assembled from:i. the entries with katakana headwords
or readings in the JMdict dictionary;ii. the entries with katakana headwords
in the Kenkyūsha New JapaneseEnglish Dictionary;iii. the katakana entries in the Eijiro dictionary database;2iv. the katakana entries in a number oftechnical glossaries covering biomedical topics, engineering, ﬁnance, law,etc.;
v. the named-entities in katakana
from the JMnedict named-entity
database.3
This dictionary, which contains bothbase words and MWEs, includes short
English translations which, where appropriate, have been split into identiﬁable
senses. It contains a total of 270,000 entries.
b. a collection of 160,000 katakana words
drawn from the headwords of the dictionary above. It has been formed by splitting the known MWEs into their components where this can be carried out reliably;c. the Google English n-gram corpus4 . This
contains 1-grams to 5-grams collectedfrom the Web in 2006, along with fre2 http://www.eijiro.jp/e/index.htm
3 http://www.csse.monash.edu.au/~jwb/

enamdict_doc.html
4 http://www.ldc.upenn.edu/Catalog/
CatalogEntry.jsp?catalogId=LDC2006T13

ソーシャル・ブックマーク・サービス
ソーシャル・ブックマーク・サー・ビス
ソーシャル・ブック・マーク・サービス
ソーシャル・ブック・マーク・サー・ビス
ソー・シャル・ブックマーク・サービス
ソー・シャル・ブックマーク・サー・ビス
ソー・シャル・ブック・マーク・サービス
ソー・シャル・ブック・マーク・サー・ビス

Table 1: Segmentation Example

quency counts. In the present project we
use a subset of the corpus consisting onlyof case-folded alphabetic tokens.
The process of segmenting an MWE andderiving a translation is as follows:a. using the katakana words in (b) above,generate all possible segmentationsof the MWE. A recursive algorithm
is used for this.
Table 1 shows
the segments derived for the MWE
ソーシャルブックマークサービス
sōsharubukkumākusābisu “social bookmark service”.
b. for each possible segmentation of an
MWE, assemble one or more possible
glosses as follows:i. take each element in the segmented
MWE, extract the ﬁrst gloss in the
dictionary and assemble a composite
potential translation by simply concatenating the glosses. Where there
are multiple senses, extract the ﬁrst
gloss from each and assemble all
possible combinations. (The ﬁrst
gloss is being used as lexicographers
typically place the most relevant
and succinct translation ﬁrst, andthis has been observed to be often the most useful when building
composite glosses.) As examples, forソーシャル・ブックマーク・サービス
the element サービス has two senses“service” and “goods or services
without charge”, so the possible
glosses were “social bookmark service” and “social bookmark goods
or services without charge”. For
ソーシャル・ブック・マーク・サービス
the element マーク has sensesof “mark”, “paying attention”,

“markup” and “Mach”, so thepotential glosses were “social bookmark service”, “social book markup
service”, “social book Mach service”, etc. A total of 48 potential
translations were assembled for thisMWE.
ii. where the senses are tagged as being aﬃxes, also create combinations
where the gloss is attached to thepreceding or following gloss as appropriate.
iii. if the entire MWE is in the dictionary, extract its gloss as well.
It may seem unusual that a single senseis being sought for an MWE with polysemous elements. This comes about because in Japanese polysemous loanwords
are almost always due to them being derived from multiple source words. For
example ランプ raNpu has three senses
reﬂecting that it results from the borrowing of three distinct English words:“lamp”, “ramp” and “rump”. On theother hand, MWEs containing ランプ,
such as ハロゲンランプ harogeNraNpu“halogen lamp” or オンランプ oNraNpu“on-ramp” almost invariably are associated with one sense or another.
c. attempt to match the potential translations with the English n-grams,
and where a match does exist, extract the frequency data.
For theexample above, only “social bookmark service”, which resulted from theソーシャル・ブックマーク・サービス
segmentation, was matched successfully;
d. where match(es) result, choose the one
with the highest frequency as both themost likely segmentation of the MWE
and the candidate translation.
The approach described above assumes thatthe term being analyzed is a MWE, when infact it may well be a single word. In thecase of as-yet unrecorded words we would expect that either no segmentation is acceptedor that any possible segmentations have relatively low frequencies associated with the potential translations, and hence can be ﬂagged
for closer inspection. As some of the testing
described below involves deciding whether aterm is or is not a MWE, we have enabled thesystem to handle single terms as well by checking the unsegmented term against the dictionary and extracting n-gram frequency counts
for the glosses. This enables the detection andrejection of possible spurious segmentations.
As an example of this, the word ボールト
bōruto “vault” occurs in one of the test ﬁlesdescribed in the following section. A possible segmentation (ボー・ルト) was generated
with potential translations of “bow root” and“baud root”. The ﬁrst of these occurs in the
English 2-grams with a frequency of 63, however “vault” itself has a very high frequency
in the 1-grams so the segmentation would be
rejected.
As pointed out above, a number of katakana
words are not loanwords. For the mostpart these would not be handled by the
CLST segmentation/translation process as
they would not be reduced to a set of known
segments, and would be typically reported
as failures. The transliteration approaches
in earlier studies also have problems withthese words. Some of the non-loanwords,
such as scientiﬁc names of plants, animals,
etc. or words written in katakana for emphasis, can be detected and ﬁltered prior to attempted processing simply by comparing the
katakana form with the equivalent hiragana
form found in dictionaries. Some of the occurrences of Chinese and Japanese names intext can be detected at extraction time, assuch names are often written in forms such as“...金鍾泌(キムジョンピル)...”.5

5 Evaluation
Evaluation of the CLST system was carriedout in two stages: testing the segmentation
using data used in previous studies to ensure it
was discriminating between single loanwords
and MWEs, and testing against a collectionof MWEs to evaluate the quality of the translations proposed.
5.1 Segmentation
The initial tests of CLST were of the segmentation function and the identiﬁcationof single words/MWEs.
We were fortu5 Kim Jong-Pil, a former South Korean politician.

Method
CLST
MeCab
CLST
MeCab

Set
EDR
EDR
NTCIR-2
NTCIR-2

Recall
98.67
92.67
94.87
82.05

Precision
100.0097.20
100.00
92.75

F
99.33
94.88
97.37
87.07

Table 2: Results from Segmentation Tests

nate to be able to use the same data usedby Bilac and Tanaka (2004), which consisted of 150 out-of-lexicon katakana terms
from the EDR corpus (EDR, 1995) and 78
from the NTCIR-2 test collection (Kando etal., 2001). The terms were hand-marked
as to whether they were single words or
MWEs. Unfortunately we detected someproblems with this marking, for example
シェークスピア shēkusupia “Shakespeare”
had been segmented (shake + spear) whereas
ホールバーニング hōrubāniNgu “hole burning” had been left as a single word. We considered it inappropriate to use this data without amending these terms. As a consequenceof this we are not able to make a direct comparison with the results reported in Bilac andTanaka (2004). Using the corrected data weanalyzed the two datasets and report the results in Table 2. We include the results fromanalyzing the data using MeCab/UniDic aswell for comparison. The precision and recall achieved was higher than that reported
in Bilac and Tanaka (2004). As in Bilac andTanaka (2004), we calculate the scores as follows: N is the number of terms in the set, cis the number of terms correctly segmented
or identiﬁed as 1-grams, e is the number ofterms incorrectly segmented or identiﬁed, andn = c + e. Recall is calculated as Nc , precision
as nc , and the F-measure as 2×precision×recall
precision+recall .
As can be seen, our CLST approach has
achieved a high degree of accuracy in identifying 1-grams and segmenting the MWEs.
Although it was not part of the test, it also
proposed the correct translations for almost
all the MWEs. The less-than-perfect recall
is entirely due to the few cases where either
no segmentation was proposed, or where theproposed segmentation could not be validatedwith the English n-grams.
The performance of MeCab/UniDic is interesting, as it also has achieved a high level ofaccuracy. This is despite the UniDic lexicon
only having approximately 55,000 katakana
words, and the fact that it is operating outside the textual context for which it has been
trained. Its main shortcoming is that it
tends to over-segment, which is a contrast to
the performance of ChaSen/IPADIC reported
in Bilac and Tanaka (2004) where undersegmentation was the problem.
5.2 Translation
The second set of tests of CLST was directed
at developing translations for MWEs. The
initial translation tests were carried out on
two sets of data, each containing 100 MWEs.
The sets of data were obtained as follows:a. the 100 highest-frequency MWEs wereselected from the Google Japanese 2grams. The list of potential MWEs
had to be manually edited as the 2grams contain a large number of oversegmented words, e.g. アイコン aikoN“icon” was split:
アイコ+ン, andオークション ōkushoN “auction” wassplit オーク+ション;
b. the katakana sequences were extracted
from a large collection of articles from1999 in the Mainichi Shimbun (aJapanese daily newspaper), and the 100
highest-frequency MWEs extracted.
After the data sets were processed by CLST
the results were examined to determine if
the segmentations had been carried out correctly, and to assess the quality of the proposed translations. The translations were
graded into three groups: (1) acceptable asa dictionary gloss, (2) understandable, but inneed of improvement, and (3) wrong or inadequate. An example of a translation gradedas 2 is マイナスイオン mainasuioN “minusion”, where “negative ion” would be better,and one graded as 3 is フリーマーケット
furīmāketto “free market”, where the correcttranslation is “ﬂea market”. For the mostpart the translations receiving a grading of 2
were the same as would have been produced
by a back-transliteration system, and in manycases they were the wasei eigo constructions
described above.
Some example segmentations, possible
translations and gradings are in Table 3.

MWE
ログインヘルプ
ログインヘルプ
キーワードランキング
キーワードランキング
キャリアアップ
キャリアアップ
キャリアアップ
キャリアアップ
キャリアアップ

Segmentation
ログイン・ヘルプ
ログ・イン・ヘルプ
キーワード・ランキング
キー・ワード・ランキング
キャリア・アップキャリア・アップキャリア・アップキャリア・アップキャリア・アップPossible Translation
login help
log in help
keyword ranking
key word ranking
career up
carrier up
career close up
career being overcarrier increasing

Frequency
541097169972
39818
74
13043
2552
195
188
54

Grade
1
1
2
-

Table 3: Sample Segmentations and Translations

Data
Set
GoogleMainichi (Set 1)
Mainichi (Set 2)

FailedSegmentations
9
3
1

Translation Grades
1
2
3
66 24
1
77 19
1
83 16
0

Precision
98.90
98.97
100.00Recall
90.00
96.00
99.00

F
94.24
97.46
99.50

Table 4: Results from Translation Tests

The assessments of the segmentation andthe gradings of the translations are given in
Table 4. The precision, recall and F measures
have been calculated on the basis that a gradeof 2 or better for a translation is a satisfactory
outcome.
A brief analysis was conducted on samples of 25 MWEs from each test set to ascertain whether they were already in dictionaries, or the degree to which they were suitable for inclusion in a dictionary. The dictionaries used for this evaluation were the commercial Kenkyusha Online Dictionary Service6 which has eighteen Japanese, JapaneseEnglish and English-Japanese dictionaries in
its search tool, and the free WWWJDIC online dictionary,7 which has the JMdict and
JMnedict dictionaries, as well as numerous
glossaries.
Of the 50 MWEs sampled:a. 34 (68%) were in dictionaries;b. 11 (22%) were considered suitable for inclusion in a dictionary. In some cases thegenerated translation was not consideredappropriate without some modiﬁcation,i.e. it had been categorized as “2”;c. 3 (6%) were proper names (e.g. hotels,
6 http://kod.kenkyusha.co.jp/service/
7 http://www.edrdg.org/cgibin/wwwjdic/wwwjdic?1C

software packages);d. 2 (4%) were not considered suitable forinclusion in a dictionary as they were simple collocations such as メニューエリア
menyūeria “menu area”.
As the tests described above were carriedout on sets of frequently-occurring MWEs, it
was considered appropriate that some further
testing be carried out on less common loanword MWEs. Therefore an additional set of
100 lower-frequency MWEs which did not occur in the dictionaries mentioned above wereextracted from the Mainichi Shimbun articles
and were processed by the CLST system. Of
these 100 MWEs:
a. 1 was not successfully segmented;
b. 83 of the derived translations were classiﬁed as “1” and 16 as “2”;c. 8 were proper names.
The suitability of these MWEs for possible inclusion in a bilingual dictionary
was also evaluated.
In fact the overwhelming majority of the MWEs wererelatively straightforward collocations, e.g.
マラソンランナー marasoNraNnā “marathon
runner” and ロックコンサート rokkukoNsāto “rock concert”, and were deemed to
be not really appropriate as dictionary entries. 5 terms were assessed as being dic-tionary candidates. Several of these, e.g.
ゴールドプラン gōrudopuraN “gold plan”
and エースストライカー ēsusutoraikā “acestriker” were category 2 translations, andtheir possible inclusion in a dictionary would
largely be because their meanings are notreadily apparent from the component words,
and an expanded gloss would be required.
Some points which emerge from the analysisof the results of the tests described above are:a. to some extent, the Google n-gram test
data had a bias towards the types of
constructions favoured by Japanese webpage designers, e.g. ショッピングトップ
shoppiNgutoppu “shopping top”, which
possibly inﬂated the proportion of translations being scored with a 2;
b. some of the problems leading to a failure
to segment the MWEs were due to theway the English n-gram ﬁles were constructed. Words with apostrophes weresplit, so that “men’s” was recorded asa bigram: “men+’s”. This situation is
not currently handled in CLST, which
led to some of the segmentation failures,
e.g. with メンズアイテム meNzuaitemu“men’s item”;

reﬁnement and experimentation on possible
enhancements can be carried out. They include:
a. instead of using the “ﬁrst-gloss” heuristic, experiment with using all available
glosses. This would be at the price of increased processing time, but may improve
the performance of the segmentation andtranslation;b. align the searching of the n-gram corpus
to cater for the manner in which words
with apostrophes, etc. are segmented. At
present this is not handled correctly;
c. tune the presentation of the glosses in the
dictionaries so that they will match better with the contents of the n-gram corpus. At present the dictionary used issimply a concatenation of several sources,and does not take into account suchthings as the n-gram corpus having hyphenated words segmented;
d. extend the system by incorporating a
back-transliteration module such as thatreported in Bilac and Tanaka (2004).
This would cater for single loanwords andthus provide more complete coverage.

6

References

Conclusion and Future WorkIn this paper we have described the CLST
(Corpus-based Loanword Segmentation andTranslation) system which has been developed
to segment Japanese loanword MWEs andconstruct likely English translations. The system, which leverages the availability of largebilingual dictionaries of loanwords and English n-gram corpora, is achieving high levels of accuracy in discriminating between single loanwords and MWEs, and in segmenting
MWEs. It is also generating useful translations of MWEs, and has the potential to being a major aide both to lexicography in thisarea, and to translating.
The apparent success of an approach basedon a combination of large corpora and relatively simple heuristics is consistent with theconclusions reached in a number of earlier investigations (Banko and Brill, 2001; Lapata
and Keller, 2004).
Although the CLST system is performing at
a high level, there are a number of areas where

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