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A Web Corpus and Word Sketches for Japanese
Irena Srdanović Erjavec† , Tomaž Erjavec†† and Adam Kilgarriﬀ†††
Of all the major world languages, Japanese is lagging behind in terms of publicly
accessible and searchable corpora. In this paper we describe the development of
JpWaC (Japanese Web as Corpus), a large corpus of 400 million words of Japanese
web text, and its encoding for the Sketch Engine. The Sketch Engine is a web-based
corpus query tool that supports fast concordancing, grammatical processing, ‘word
sketching’ (one-page summaries of a word’s grammatical and collocational behaviour),a distributional thesaurus, and robot use. We describe the steps taken to gather andprocess the corpus and to establish its validity, in terms of the kinds of language it
contains. We then describe the development of a shallow grammar for Japanese toenable word sketching. We believe that the Japanese web corpus as loaded into the
Sketch Engine will be a useful resource for a wide number of Japanese researchers,
learners, and NLP developers.
Key Words: Japanese web corpus, Corpus query tool, Sketch Engine, Word sketches

1

The Sketch Engine
Of all the major world languages, Japanese is lagging behind in terms of publicly accessible andsearchable corpora. This paper reports on the development of JpWaC (Japanese Web as Corpus),a large corpus of Japanese web text, which has been loaded into the Sketch Engine, where it takes
its place alongside Chinese, English, French, German, and a range of other languages.
The Sketch Engine1 (Kilgarriﬀ et al. 2004) is a corpus tool with several distinctive features.
It is fast, giving immediate responses for most regular queries for corpora of up to two billion
words. It is designed for use over the web. It works with all standard browsers, so users need notechnical knowledge, and do not need to install any software on their machine. It has been used
for dictionary compilation at, amongst others, Oxford University Press2 , Macmillan (Kilgarriﬀ
and Rundell 2002), Chambers Harrap and Collins, and also for language teaching (e.g. Chenet al. 2007) and language technology development (e.g. Gatt and van Deemter 2006; Chantree
et al. 2005).
As well as oﬀering standard corpus query functions such as concordancing, sorting, ﬁltering† Tokyo Institute of Technology†† Jožef Stefan Institute††† Lexical Computing Ltd. and Universities of Leeds and Sussex
1 http://www.sketchengine.co.uk

2 http://www.askoxford.com/oec

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etc., the Sketch Engine is unique in integrating grammatical analysis, which makes it possible toproduce word sketches, one-page summaries of a word’s grammatical and collocational behaviour
as illustrated in Figure 1.
Figure 1 gives a word sketch for the noun お湯 (oyu). Diﬀerent grammatical relations, such as
Ai and Ana adjectives modifying a noun, noun-particle-verb collocates (を verb, で verb, が verb),
noun-pronominal relations (with の) etc., are displayed in order of their signiﬁcance, revealing
the most frequent and most salient sets of collocations (the ﬁrst and second column with numbers
respectively). The list of collocates nicely reveals the diﬀerent senses of the noun: (1) ‘hot/warm
water (water that is heated)’ （お湯を沸かす，熱いお湯，ポットのお湯，鍋のお湯，お湯で茹でる，お湯で温める），(2) ‘bath (water for taking a bath)’ （お湯に入る，お湯から上がる，湯Fig. 1 Word sketch for お湯 (oyu ‘hot water’). The ﬁrst number is the number of instances forthe collocation. The second is the association score (based on the Dice coeﬃcient) for thecollocation, which is used for deciding which collocations to show and sorting.

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A web corpus and word sketches for Japanese

舟のお湯，浴槽のお湯，風呂のお湯，易しいお湯，いいお湯）3 The “coord” relation reveals thecoordinate noun of お湯，that is 水 (mizu ‘water’).
Based on the grammatical analysis, we also produce a distributional thesaurus for the language, in which words occurring in similar settings, sharing the same collocates, are put together(Sparck 1986; Grefenstette 1994; Lin 1998; Weeds and Weir 2005), and “sketch diﬀs”, which
compare related words (see section 3 below). The Sketch Engine is accessible either by a personusing a web browser, or by program, with the output from the Sketch Engine (in plain text, XML
or JSON) being an input to some other NLP process.

2

The JpWaC corpus
In this section we review the steps taken to compile the JpWaC corpus, give a quantitative

account of its contents and discuss the validity of this kind of corpus for general language research.
The corpus has been gathered using methods as described by (Sharoﬀ 2006a, 2006b; Baroni and
Kilgarriﬀ 2006), and for Japanese (Ueyama and Baroni 2005). The corpus consists of texts
obtained from around 50,000 web pages and contains just over 400 million tokens.

2.1

Compiling the corpus

For easy web corpus compilation, a great step forward has been made by the publicly available
BootCat4 (Baroni and Bernardini 2004; Baroni et al. 2006), WAC5 (Web as Corpus Toolkit) andrelated work of the Wacky Project6 (Baroni and Bernardini 2006). These open source tools provide
the functionality for compiling a basic web corpus, and are straightforward to install and use on
a Linux platform.
The main steps for compiling JpWaC were the following, explained in more detail below:(1) obtain list of Japanese language URLs
(2) download the Web pages(3) normalize character encodings(4) extract metadata(5) remove document boilerplate(6) annotate with linguistic information
3 Checking word sketches for the variant 湯 (yu), reveals an additional sense, ‘hot spring’, that is not present in

the お湯 (oyu) variant.

4 http://sslmit.unibo.it/˜baroni/bootcat.html
5 http://www.drni.de/wac-tk/

6 http://wacky.sslmit.unibo.it/3

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1. The list of URLs to build the corpus is critical for any web corpus, as it determines theoverall composition, in terms of language(s), registers, lexis, etc. In order to obtain a good corpus
of general language, the URL list for JpWaC list was obtained via the methodology describedin (Sharoﬀ 2006a). First, the top 500 non-function words of the British National Corpus (seehttp://natcorp.ox.ac.uk; hereafter BNC) were translated into Japanese7 . Random 4-tuples weregenerated from this list and the Google API was used to query the internet with them. The setof retrieved pages gave the ﬁnal URL list.
2. We downloaded the HTML pages with the WAC toolkit program paraget, which implements parallel downloading of pages, while taking care not to overload particular servers; it also
separates the large number of retrieved ﬁles into separate tree-structured directories.
3. For normalizing the character sets of the downloaded pages we used the BootCat tool
encoding-sort.pl, which implements a heuristic to guess the encoding of each HTML page,
and then the standard UNIX utility iconv to convert the actual ﬁle to UTF-8.
4. The extraction of meta-data implemented in BootCat/WAC, in particular title and author,
did not work well on Japanese pages, so the only metadata we retained is the URL of eachdocument.
5. For boilerplate removal we used BootCat clean pages from file list.pl, which removes
HTML tags, JavaScript code and text-poor portions of the pages, such as navigational frames,leaving only the pure text of each page.
6. Finally, we annotated the corpus using ChaSen8 , which segments the text into sentences,
tokenises it, lemmatises the words and assigns them morphosyntactic descriptions (MSDs). The
MSDs that ChaSen returns are in Japanese: to make it easier to understand and use the annotations by non-Japanese speakers (and those without Japanese language keyboards), we translated
the Japanese MSDs into English.

2.2

Copyright

There has been much discussion amongst corpus-builders about copyright. Many have taken
the view that, for any text to be included, the copyright holder must be contacted and the
document can only be included if their consent is granted.
This perspective dates back to a pre-web era. A corpus tool such as Sketch Engine, when
loaded with a web corpus, is performing a task that is equivalent to a search engine such as Google
or Yahoo. In each case, the owners of the service have automatically gathered large numbers of
7 The words were translated by Moto Ueyama.
8 http://chasen.naist.jp/

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A web corpus and word sketches for Japanese

web pages, processed them, and indexed them. Then, users are shown small extracts.
This basic function of search engines has not been legally challenged. Google and Yahoo do
not ask copyright owners for permission to index their pages. They, like us, observe the “norobots” convention for not trawling pages where the owner has speciﬁed in a “robots.txt” ﬁlethat they should not.
Various related search engine functions have been challenged, such as where Google preserves,and shows users, cached copies of pages which are no longer freely available because the owner ofthe material charges for access to their archive. There have also been challenges where copyrighted
videos have been placed on YouTube: YouTube oﬀers the defence that it did not know that thematerial was there, which is an acceptable defence (in US law) provided that YouTube promptly
removes the oﬀending material when asked to do so. The current issue in this case (YouTube
vs. Viacom, a large case which is ongoing as at October 2007) is how quickly and eﬀectively
YouTube responds, when the copyright owner brings it to YouTube’s attention that it is hosting
copyrighted material. Taking note of such cases, Lexical Computing Ltd has mechanisms forpromptly removing copyrighted material from its corpora should a copyright owner objects.
There is no legal question over the legitimacy of the core activity of commercial search engines,or, thereby, of the legitimacy of presenting web corpora in the Sketch Engine.

2.3

Corpus statistics

In this section we give some statistics over the corpus in terms of overall size, the number of
web sites and documents, and the statistics over linguistic categories.
The total corpus size is 7.3 GB, or 2 GB if compressed (.zip). An overview of the corpus size interms of web documents is given in the left side of Table 1. The documents, just under 50 thousand, correspond to Web pages, each one identiﬁed by its URL, e.g. http://www.arsvi.com/0e/
ps01.htm. The number of hosts (16,000) corresponds to distinct Web sites, such as http://www.
arsvi.com. Dividing the former by the latter gives us the average number of pages per host, 3.1.
The last two lines in the table give the numbers of pages from each of the two domains present
in the corpus, with three quarters of the pages being from .jp and one quarter from .com.
It is also interesting to see what kinds of websites the documents come from. The center ofTable 1 gives the keywords, deﬁned as alphabetic strings appearing in URLs, which cover morethan a thousand documents. The keywords to a certain extent reﬂect the Web sites with the greatest number of documents, in particular blog.livedoor.jp (1,646 documents), www.amazon.co.jp(1,048 documents), d.hatena.ne.jp (759 documents), and blog.goo.ne.jp (690 documents).
Linguistic processing with ChaSen gave us 12,759,201 sentences and 409,384,411 tokens; statis5

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Table 1n
49,544
16,072
3.1
34,911
14,633

What
Docs
Hosts
Docs/host
URL .jp
URL .com

Docs
6,486
3,471
2,362
2,075
1,545
1,428
1,380
1,296
1,051
1,013
1,006

Apr. 2008

Corpus statistics

URL Keyword
Blog
Nifty
Archives
Livedoor
Diary
News
Cocolog
ExblogAmazonArchive
Geocities

Tokens/Doc
8,263
5,001
3
60,929Stats
Average
MedianMin
Max

tics over tokens per document is given on the right side of Table 1.

2.4

JpWaC validity, and comparison with a newspaper corpus

At this point, the reader will naturally want to know what sort of language there is in the
web corpus. People are often concerned that web corpora will give a partial and distorted viewof a language. These are diﬃcult concerns to address because there is no simple way to describe
a collection, which is, by design, heterogeneous, and where there has been no process of assigninglabels like “journalism” or “novel”, to individual pages. This is a central problem in corpus
linguistics (Kilgarriﬀ 2001).
One straightforward strategy is to make comparisons with other corpora. As newspaper
corpora have been widely used in Japanese language research and we had at our disposal thedata from Mainichi Shimbun newspaper for the year 2002 (hereafter News), we compared JpWaCwith that. The raw News corpus was processed with ChaSen, in the same way as JpWaC, and hasaround 30 million tokens. We analyzed the diﬀerences between the two corpora with frequency
proﬁling, as described in (Rayson and Garside 2000). The method can be used to discover items
of interest (say, key words or grammatical categories) in the corpora, which diﬀerentiate one
corpus from another. In our case, we used it to discover lexical items (word form + ChaSen tag)or just tags in isolation. The method is straightforward: we ﬁrst produce frequency lists and thencalculate the log-likelihood statistic for each item. LL takes into account the two frequencies of
the item and the sizes of the respective corpora and the larger it is, the more the item is salientfor one of the two corpora. Starting with the items that have the highest LL scores, it is thenpossible to investigate the major diﬀerences between JpWaC and News.
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The highest LL scores resulted from diﬀerences in coding practices. Both corpora use Unicode,
which allows for coding the alphanumeric characters and basic punctuation in the standard ASCII
range or, for Japanese, in the “Halfwidth Katakana” block (U+FF00 – U+FFEF). Similarly, thespace character exists in ASCII, but also as the Unicode character “Ideographic space” U+3000.
This space (analysed as a token with the tag Sym.w by ChaSen) is the item with the highest LL
score. It appears over 24 times per 1000 words in News, while hardly ever in JpWaC. JpWaC
uses, for the most part, ASCII characters for Western spaces, letters and numbers, while News
uses their Eastern Unicode equivalents. Furthermore, ChaSen tokenises words written in ASCII
into individual characters (and tags them with Sym.a). Being unaware of such diﬀerences canhave practical consequences in cases where a researcher is interested in patterns including thesecharacters.
Table 2 shows the twenty most salient log-likelihood diﬀerences in ChaSen tags between thetwo corpora. As explained, the ﬁrst one marks Ideographic space, the second ASCII letters, andthe third (Sym.g) ASCII punctuation. The fourth shows that unknown words (i.e. words thatare not in the ChaSen dictionary) are much more frequent on the web than in the newspaper
corpus. These three high LL scores thus stem for a combination of diﬀerent coding practices,
coupled with diﬀerences in ChaSen processing. The ﬁfth row (N.Num) shows that numerals are
more frequent in News. (This is a true diﬀerence in content, as ChaSen correctly tags both ASCII
numbers and their Eastern Unicode equivalents with N.Num.) More frequent in News are alsomeasure suﬃxes and proper nouns. Bounding nouns, pronouns, auxiliaries and adverbs are much
more frequent in JpWaC.

Table 2 Diﬀerences in ChaSen tags between the JpWaC and News (ﬁrst 20 tags). The ﬁrst column gives
the ChaSen tag, the second the log-likelihood score, and the third and fourth the frequencies
per thousand words
Tag
Sym.w
Sym.a
Sym.g
Unknown
N.Num
N.Suﬀ.msr
N.Prop.p.g
N.Prop.oN.bnd.g
N.Prop.n.sLL39436413757462526014413833367879308487238821216779206389204396

JpWaC
0.02
67.38
32.52
14.85
49.24
11.10
4.46
2.26
18.52
3.62

News
24.54
0.71
11.53
2.99
76.65
23.88
11.98
7.65
8.31
9.91

Tag
N.Suﬀ.p
N.Pron.g
Aux
N.Vs
P.ConjN.Prop.p.c
Adv.g
N.Suﬀ.g
Sym.boAdn

7

LL179445167407156444
153786122523111389
99256
86295
85666
85075

JpWaC
0.76
9.97
70.35
53.00
32.72
3.57
8.8
16.97
14.81
7.76

News
3.97
3.44
51.72
70.82
21.67
7.96
3.92
24.65
21.99
3.51

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In JpWaC the following forms / grammatical categories are more salient than in News:• auxiliary verbs forms ます，です，まし，ませ showing that sentence endings in the web
data, in contrast to the newspaper data, frequently uses the masu/desu form
• forms expressing modality か，でしょ，よう，思う，ので，わけ• forms expressing politeness お，ござい• forms expressing informal language って，よ，ね，ん• person and place deixis: the ﬁrst personal pronoun 私， and place diexis この，その，それ• noun bound forms こと and もの
In the newspaper corpus the following are more salient than in JpWaC:• past tense form た showing that the newspaper data is mainly written in the past tense,in contrast to the web data.
• various suﬃxes and preﬁxes expressing time, place, numerals, measures, people 日，市，県，役，円，メートル，際，人，さん• adverbial nouns expressing time 昨年，後，元• proper nouns 東京，大阪，鈴木• general nouns that are speciﬁc for newspapers content and politically oriented 容疑，首相，米国，テロ，自民堂 (No nouns that are speciﬁc to web data occurred amongst the 100

highest-LL entries. We believe this is because Web data is more heterogeneous, so, while
there are very many nouns which have higher relative frequency in JpWaC than News,
none had high enough frequency and bias to reach the ‘top 100’.)
Table 3 gives the twenty words (together with their ChaSen tag) that have the highest LL
scores. Based on the top hundred words, the analysis reveals a number of interesting diﬀerences
between the corpora.
The comparison of the two corpora shows that newspaper data is more speciﬁc both in termsof form (being written mainly in past tense and not using masu/desu), as well as content (highproportion of news speciﬁc nouns). On the other hand, JpWaC contains more informal andinteractional material, and more diverse content. The distinction between formal, distanced
language and informal, interactional language is the most salient dimension of language variation,
as has been explored and established in a number of studies by Biber and colleagues (Biber 1988,
1995), see also (Heylighen 2002). The eﬀect for JpWaC matches the diﬀerences that (Sharoﬀ
2006a) found when comparing web corpora with newspaper corpora for English and German.
For English, he also considers a third point of comparison: the BNC, a corpus carefully designed
to give a balanced picture of contemporary British English and often cited as a model for othercorpus-building projects. Sharoﬀ ﬁnds that his web corpus (prepared using the same method we
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A web corpus and word sketches for Japanese

Table 3 Diﬀerences in entries between JpWaC (left table) and News (right table): the most distinctive
twenty words, according to the log-likelihood statistic (LL), in each case. Columns JpWaC
and News give frequencies per thousand for the two corpora.
word
form

tag

LL

JpWaC

News

word
form

tag

LL

JpWaC

News

ます
です
まし
月
て
で
か
の
な
ん
こと
ん
ませ
私
ござい
その
もの
それ
よう
という

Aux
Aux
Aux
N.Suﬀ.msr
P.ConjAux
P.advcoordﬁnN.bnd.gAux
N.bnd.gN.bnd.g
Aux
Aux
N.Pron.g
Aux
Adn
N.bnd.g
N.Pron.g
N.bnd.Aux
P.c.Phr

206122148844
88599
65734
64966
59324
59069
58270
51056
42093
36013
34973
34454
33163
29663
28998
28935
28902
28562
28068

5.76
4.55
2.69
1.12
21.04
7.53
4.90
6.31
6.63
1.44
5.86
1.22
1.07
1.72
0.55
2.25
1.80
1.54
2.76
2.35

0.74
0.70
0.41
0.00
14.54
3.96
2.10
3.10
3.52
0.27
3.38
0.24
0.17
0.53
0.01
0.93
0.64
0.48
1.29
1.02

日
た
同
約
市
など
容疑
首相
円
人
万
勝
区
を
東京
県
で
氏
後
北朝鮮

N.Suﬀ.msr
Aux
Pref.N
Pref.Num
N.Suﬀ.p
P.Adv
N.g
N.g
N.Suﬀ.msr
N.Suﬀ.msr
N.Num
N.Suﬀ.msr
N.Suﬀ.p
P.c.g
N.Prop.p.g
N.Suﬀ.p
P.c.g
N.Suﬀ.n
N.g
N.Prop.p.c

110177104951
93209
68451
64868
64244
63021
54740
48315
46548
41995
40016
39628
38881
38117
37005
36297
33179
33120
32973

1.38
16.95
0.12
0.17
0.19
1.22
0.02
0.05
0.48
0.80
0.38
0.01
0.08
21.58
0.24
0.17
8.79
0.28
0.03
0.05

4.34
25.5
1.26
1.20
1.20
3.25
0.58
0.67
1.67
2.22
1.37
0.35
0.64
27.2
1.03
0.85
12.4
1.04
0.40
0.48

have used) is more similar to the BNC, than either of them are to the newspaper corpus. Thissuggests that JpWaC gives a fuller picture of current Japanese than do newspaper corpora such
as Mainichi Shimbun.
The ﬁndings are in accordance with several other studies that explore the nature of web corpora. (Kilgarriﬀ and Grefenstette 2003) review the ﬁeld and show that approved or correct forms
are typically orders of magnitude more common on the web than incorrect or disparaged forms.
In (Keller and Lapata 2003) they undertake psycholinguistic studies which show that associations
between pairs of words as found on the web, using search engine hit-counts, correspond closely
to associations in the mental lexicon (and for rarer items, the web associations do a better job
of approximating mental-lexicon associations than do extrapolations from the BNC).
The issue of corpus composition is crucial for many potential users of the corpus. Consider
for example dictionary publishers, who are perhaps the users with the greatest need for large,balanced corpora. (The impetus for the BNC came from dictionary publishing at Oxford University Press and Longman.) For them, newspaper corpora are inadequate because they include
only journalism, and journalism is a relatively formal text type. Dictionaries need to cover thevast number of informal words, phrases and constructions that are common in the spoken lan-9

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guage but which rarely make it into newspapers. They would like the corpora they use to be
not only very large, but also to include a substantial proportion of informal language. In the
BNC, this was addressed by gathering and transcribing (at great expense) ﬁve million words ofconversational spoken English.
Size is also an issue. At 100 million words, the BNC was, in the early 1990s, a vast resource,which lexicographers and other language researchers revelled in. But ﬁfteen years on, it no longer
looks large when compared to the web, and lexicographers who have been using it extensively at
Oxford University Press and Longman, are vividly aware that for some phenomena, it does notprovide enough data. The leading UK dictionary-makers are now all working with larger (andmore modern) corpora, of between 200 million and 2 billion words9 . At 400 million words ofheterogenous Japanese, JpWaC is a good size for contemporary dictionary making.
Our initial investigations suggest that JpWaC will be a good corpus, in terms of size, composition, and availability, for Japanese lexicography. For the same reasons it will be a useful
resource for exercises such as the preparation of word lists for Japanese language teaching andother research requiring good coverage of current Japanese vocabulary across the full range ofgenres.

2.5

Other Japanese web corpora

We are aware of two Japanese web corpora that are comparable to ours. Ueyama and Baroni(2005) used very similar methods. The diﬀerences are simply that their corpus is smaller (at 62
million words) and that they also classify the corpus in terms of topic domains and genre types.
Kawahara and Kurohashi (2006) crawled the Japanese web very extensively and have prepareda corpus around ﬁve times larger than JpWaC in form of extracted sentences. The authors kindly
allowed us access to the data, and we have examined samples and believe it to be a high quality
resource, comparable in many ways to JpWaC.

3

The Sketch Engine for JapaneseIn this section we describe how JpWaC was prepared for the Sketch Engine and the develop-

ment of the shallow grammatical relation deﬁnitions to support word sketching.
Loading a corpus into the Sketch Engine enables the use of standard functions, such as concordances, which include searching for phrases, collocates, grammatical patterns, sorting concor-

9 See e.g. http://www.askoxford.com/oec

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dances according to various criteria, identifying “subcorpora” etc. In addition, after deﬁning thegrammatical relations and loading them into Sketch Engine, the tool ﬁnds all the grammatical
relation instances and oﬀers access to the more advanced functions: word sketches, thesaurus,
and sketch diﬀs.

3.1

Loading the corpus

The Sketch Engine supports loading of any corpora of any language, either on the commandline for local installations of the software, or using the ‘CorpusBuilder’ web interface. In orderto create good word sketch results, the corpus should be lemmatized and PoS-tagged. (It is also
possible to apply the tool to word forms only, which can still give useful output.)
The input format of the corpus is based on the “word per line” standard developed for
the Stuttgart Corpus Tools (Christ 1994). Each word is on a new line, and for each word,
there can be a number of ﬁelds specifying further information about the word, separated bytabs. The ﬁelds of interest here are word form, PoS-tag and lemma. Other constituents suchas paragraphs, sentences and documents, can be added with associated attribute-value pairs in
XML-like format10 . An example is given in Figure 2.
As described above, JpWaC had been lemmatized and part-of-speech tagged with the ChaSen
tool. It was then converted into word-per-line format.

3.2

Preparing grammatical relations

For producing word sketches, thesaurus and sketch diﬀs grammatical relations need to bedeﬁned. This mini-grammar of syntactic patterns enables the system to automatically identify
possible relations of words to a given keyword. It is language and tagset-speciﬁc. The formalism
uses regular expressions over PoS-tags. As an example we give below a simple deﬁnition for an
adjective modiﬁer relation:=modifies
2:[tag= ‘‘Ai’’] 1:[tag=‘‘N.*’’]The grammatical relation states that, if we ﬁnd an adjective (PoS tag Ai) immediately followed
by a noun (PoS tag starts with N), then a modifies relation holds between the noun (labelled 1)
and the adjective (labelled 2).
A more complex deﬁnition, as used in JpWaC, is given below. Here we deﬁne a pair ofrelations, modifier Ai and modifies N, which are duals of each other: if w1 is in the relation

10 Full documentation is available at the Sketch Engine website (http://www.skechengine.co.uk).

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Apr. 2008<doc id="http://www.0start-hp.com/voice/index.php">


月々
月々
N.Adv
２
２
N.Num
６
６
N.Num
３
３
N.Num
円
円
N.Suff.msr
で
だ
Aux
、
、
Sym.c
あなた
あなた
N.Pron.gもも
P.bindブログデビュー ブログデビュー Unknown
しする
V.freeててP.Conj
み
みる
V.bnd
ませ
ます
Aux
ん
ん
Aux
か
か
P.advcoordfin
？
？
Sym.g


Fig. 2 An example stretch of the corpus

modifier Ai to w2, then w2 is in the relation modifies N to w1. We start by declaring that wehave two ‘dual’ relations, and give them their names. Then comes the pattern itself, with the twoarguments for the two relations labelled 2 and 1. The pattern excludes the nai adjectival form
(using the operator “!”) and possibly includes a preﬁx before the noun (using the operator “?”).
Since the tag “N.*” includes also suﬃxes and bound nouns, we exclude these from the results.
*DUAL=modifier Ai/modifies N
2:[tag=‘‘Ai.*’’ & word!=‘‘ない|無い’’] [tag=‘‘Pref.*’’]?
1:[tag=‘‘N.*’’ & tag!=‘‘N.Suff.*’’ & tag!=‘‘N.bnd.*’’]The Sketch Engine ﬁnds all matches for these patterns in the corpus and stores them ina database, complete with the corpus position where the match was located. The database isused for preparing (at run time) word sketches and sketch diﬀerences and (at compile time) a
thesaurus. When the user calls up a word sketch for a noun, the software counts how often
each adjective occurs in the modifier Ai relation with the noun, and computes the salience of theadjective for the noun using a salience formula based on the Dice co-eﬃcient. If the adjective is

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A web corpus and word sketches for Japanese

above the salience threshold,11 it appears in the word sketch.
As mentioned, the Japanese grammatical relations are prepared using the ChaSen PoS tags
and tokens. The ChaSen tags are quite detailed (88 tags), and the tokenisation is “narrow”:
it splits inﬂectional morphemes from their stems. This has advantages and disadvantages in
the creation of grammatical relations. The advantage is that by using already precisely deﬁnedtags and small tokens, it is easier to deﬁne desired patterns and the need to specify additional
constraints is lower. The main disadvantage is that sometimes a targeted string is divided into
several tokens by the analyzer, making it more diﬃcult to deﬁne patterns and impossible toretrieve some types of results (for example, 女の人 is divided into 3 tokens, onna-no-hito, and istherefore not considered as a unit by the system). However, it is possible to search for this kindof strings in the Concordance window, using the CQL functionality.
We deﬁned 22 grammatical relations for Japanese, mostly using the “dual” type. There isalso one symmetric relation (where a match for relation R between w1 and w2 is also a matchfor R between w2 and w1, and one unary relation (involving only one word). Although thegrammatical relations for other languages in the Sketch Engine name relations by their functions,such as subjects, objects, we found it easier to remain on the level of particles (が, は, を etc.)
and avoid the complexity of topic vs. subject functions - diﬀerences between は and が particles.
A similar approach is seen in (Kawahara and Kurohashi 2006).
Since the grammatical relations formalism is sequence-based, it is better suited to languages
with ﬁxed word order, such as English. There are already some reports on addressing the problem
of free word order in creation of Czech and Slovene word sketches (Kilgarriﬀ et al. 2004; Krek andKilgarriﬀ 2006), where the simple mechanism of gaps in the patterns is employed as one of thesolutions. We also use it for the Japanese word sketches. In the example below, up to 5 tokens
are allowed, using the notation []{0,5}, between the case particle で and the corresponding verb.
*DUAL=noun で/で verb
2:[tag=‘‘N.*’’][tag=‘‘P.c.g’’ & word=‘‘で’’][ ]{0,5}1:[tag=‘‘V.*’’]|[tag=‘‘N.Vs’’][tag=‘‘V.*’’]
This kind of pattern covers an example such as:
授業

で

，

少し

だけ

講演

さ

せ

て

頂き

まし

た．

N.* P.c.g& で [ ]{0,5} N.Vs V.*. . .

11 The adjective must also occur with the noun above the frequency threshold, and must be one of the N (default= 25) highest-scoring adjectives meeting these criteria.

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While for some languages trinary relations (between three dependent items) were useful for
identifying prepositional phrases, we deﬁned prepositional phrases employing dual relations. In
this way, we were able to specify constraints relevant for a speciﬁc particle, which gave higher
precision output. On the other hand, to deﬁne grammatical relations for the phrasal verbs inJapanese (for example, to query most relevant objects and subjects of idioms, such as S が O を気に入る) the existing relations proved to be too weak, as they are limited to 3-token relations.

While these relations are not displayed in the word sketch, they can be found using the ‘sort
collocations’ functionality in the concordancer.
The grammatical relations would ideally be more sophisticated, but we have found that very
simple deﬁnitions, while linguistically unambitious, produce good results. Linguistically complex
instances are missed when using simple deﬁnitions, but it is generally the case that a small numberof simple patterns cover a high proportion of instances, so the majority of high salience collocatesare readily found, given a large enough corpus (Kilgarriﬀ et al. 2004). For Japanese, as for otherlanguages, PoS-tagging errors cause more anomalous output than do weaknesses in the grammar.
However these errors are rare and the quality of the word sketch output is good. We evaluatedthe output of six items（元気，閉める，女の子，多分，温かい，温泉）that had all together approx.
1000 collocational instances (grammatical relations’ instances) in the word sketches output. The
result showed that only seven output mistakes were due to PoS-tagging errors (for example, 温泉へ行う（おこなう）instead of 行く（いく）) and four of them were due to a shortcomingof the gramrel speciﬁcation. Fourteen cases oﬀered valuable collocational information but wereincomplete due to ChaSen’s narrow tokenization （っぽいの女の子）. Nonetheless, in futureversions, we plan to add more advanced relations into the system and cover also the instancesthat are now missed and not able to search for (for example, mutli-word units and ‘suru’ verbs).

3.3

Word sketches

The word sketch for a word presents a list of all of its salient collocates, organised by thegrammatical relations holding between word and collocate. The grammatical relations are as
named and deﬁned in the previous section, and the collocates are as found in the corpus. For
each collocate listed, the word sketch provides:• the statistical salience and frequency with which keyword and collocate occur together;• links to concordances, so we can explore the pattern by looking at the corpus examples.
The word sketch also provides links to grammatical relations, where we can see how thepattern is deﬁned inside the system.

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A web corpus and word sketches for JapaneseFig. 3 Word sketch for the verb 閉める (shimeru, ‘to close’)

We presented word sketches for the noun お湯 in the opening section; here we give anotherexample, this time for the verb 閉める (shimeru) (Figure 3). Here the grammatical relations
reveal diﬀerent noun-particle-verb relations, such as ドアを閉める，カーテンは閉める，カー
テン・扉・店が閉められる，後ろ手・鍵で閉める．We can also easily ﬁnd the most relevantbound verbs that appear with the verb: 閉めきる，閉め直す，閉めてくれる・いただく・もらう
etc. Adverbs that usually modify the verb, しっかり，きちんと，必ず imply that the action is
done/should be done ﬁrmly, tightly, deﬁnitely. The suﬃxes that appear with the verb （られる，させる，っぱなし） suggest the frequent passive and causative usage of the verb. After checking

the concordance, we can also select a number of useful usage examples:ドアがきちんと閉められています，雨戸を閉めさせる，カーテンを閉めっぱなしにする).

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Apr. 2008

Thesaurus

The semantic similarity in the Sketch Engine is based on “shared triples” (for example, 雑誌
and 本 share the same triple ?を読む ). When we ﬁnd a pair of grammatical relation instances,such as  雑誌を読む  and  本を読む  with high salience for both words, 本 and 雑誌, we
use it as a piece of evidence for assuming the words belong to the same thesaurus category. The
thesaurus is built by computing “nearest neighbours” for each word, and based on the tradition of
automatic thesaurus building (Sparck 1986; Grefenstette 1994; Lin 1998). We present a thesaurus
entry for the word お湯 in Figure 4.
As can be seen from the list of the words in the thesaurus, they can also suggest diﬀerent
senses of a word. In the case of お湯, these are ‘bath’ （風呂） and お湯 as a liquid ‘hot/warm
water’（液体，熱湯）(see also Section 1). The thesaurus also reveals that the word is semanticallymost similar to the word 湯 (yu), which is actually a variation of the お湯 (oyu), and adds on an
additional sense ‘hot spring’ （温泉）．It also oﬀers sets of related words belonging to the same
semantic domains, indicating that お湯 is semantically related to food/drink and its preparation（スープ，牛乳，お茶，紅茶，ご飯，鍋など），to water ﬂow/liquids （液体，液，海水，水道），

to bathing （シャワー，汗，湯船） etc. It also relates to 水，熱湯 and its antonym 冷水．

Fig. 4 Thesaurus for the noun お湯 (oyu)1606srdanovic :2008/6/5(10:6)

Srdanović, Erjavec and Kilgarriﬀ

3.5

A web corpus and word sketches for Japanese

Sketch Diﬀerences

The diﬀerence between two near-synonyms can be identiﬁed as the triples that have highsalience for one word, but no occurrences (or low salience) for the other. Based on this typeof data on various grammatical relations and their salience, a one-page summary for sketch
diﬀerences between two semantically similar words can be presented. The system is also usefulfor showing diﬀerences in language usage for words that are considered semantically similar
but diﬀerent orthographically, for example 良い (yoi/ii ) and いい (ii ), as well as for showing
diﬀerences of transitive/intransitive semantic pairs, such as 閉める・閉まる (shimeru/shimaru).
The sketch diﬀerence summary oﬀers the list of collocates that are common for the comparing
pair showing their salience and frequency variance, as well as the list of collocates that appear
only with one word of the comparing pair.
Figure 5 shows partial results of the sketch diﬀerence for 女の子 (onna no ko, ‘girl’) and 男の子 (otoko no ko, ‘boy’). Ai adjective-noun relations that are common to both (common patterns),

and that apply only to one of the words (“女の子” only patterns and “男の子” only patterns) aredisplayed. From the results we can see that 可愛い，いい，although common to both, is morepresent as the collocation to 女の子．“Only patterns” reveal that かわいらしい，美しい，強いcollocate only with 女の子 and that カッコイイ，かっこいい，弱い appears only with 男の子．
Before we presented word sketches for the word お湯．Comparing it with its coordinate noun
水 in the sketch diﬀerence shows clearly that only 水 collocates with 冷たい，おいしい and only
お湯 with 熱い．

Fig. 5 Sketch diﬀerence for 女の子 and 男の子 (partial)

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Apr. 2008

Evaluation
We evaluated the word sketches results by comparing them to ten randomly selected entries

in the Japanese collocational dictionary 『日本語表現活用辞典』(Himeno 2004). Here we brieﬂysummarize the results, for a detailed description please refer to (Srdanović and Nishina 2008).
The ﬁrst diﬀerence to be noticed is in the number of grammatical relations holding between
the words of a collocation, or collocation types. The Sketch Engine oﬀers a richer set. As wellas Ana adjectives and verbs, there are nouns, Ai adjectives, adverbs and others. Also for one
word class there is a richer variety of collocations. For example, for verbs, there are not justcollocations with が，を，と，に as in the dictionary, but also with で，まで，から，へ and はparticles.
The comparison also suggests that the word sketch is a useful tool for selecting the most
relevant collocations—for example, the very frequent かすかな記憶 was missed in the dictionary.
There are also some collocations in the dictionary, which were not present (or not regarded as
signiﬁcant) in the JpWaC word sketches. For verbs 閉める and 閉まる we ﬁnd collocations suchas ドア，まど，カーテン, but we do not ﬁnd 襖 and 障子 which are present in the dictionary. Thissuggests diﬀerences in the corpora used (the dictionary uses newspaper corpus, modern literatureetc.) and indicates that language change has occurred and these two collocations are no longer
current.
In addition, the dictionary usually oﬀers examples for most signiﬁcant collocations. The
Sketch Engine is useful for ﬁnding good examples because the user can click on the word in theword sketch to see a concordance of the sentences that the collocation occurs in.
Lastly, Thesaurus and Sketch Diﬀ functions can also be used to easily obtain relevant dataon similar words. This type of data can be used for cross-references and to show the diﬀerences
between similar words, which are rarely captured in the dictionary. Although it seems that 閉める and 閉まる share almost the same collocations, some diﬀerences can be rapidly found in the

Sketch Diﬀ—for example, レストラン，商店，図書館 are used only with 閉まる．
In this evaluation, we concentrated on collocational dictionaries and conﬁrmed that word
sketches are a useful tool in compiling such dictionaries. As has already been shown for otherlanguages, the application of the tool is in fact broader—in lexicography, for compilation ofvarious dictionary types, as well as for NLP, language research, and language teaching.

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5

A web corpus and word sketches for JapaneseConclusion and further work
In this paper we presented how JpWaC, a 400-million-word Japanese web corpus, and a set ofJapanese grammatical relations were created and employed inside the Sketch Engine. The Sketch
Engine uses grammatical relations (deﬁned with regular expressions over part-of-speech tags) andlexical statistics, applied to a large corpus, to ﬁnd useful linguistic information: the most salient
collocation and grammatical patterns for a word. The tool has already proved to be useful for
English and other languages, and we believe that the Japanese version of the tool is a step forward
in corpus-based lexicography, language learning, and linguistic research for Japanese. Its possible
application in the various ﬁelds is investigated and exempliﬁed in (Srdanović and Nishina 2008).
As future work, we plan to make the system user-friendly for both native-speakers and learnersof Japanese, by providing a Japanese interface and by oﬀering option to choose between English
and Japanese tag sets and grammatical relation names, and by providing the corpus also in
furigana and romaji transcriptions. We shall also enrich the grammatical relation set.
We also aim to add some other Japanese corpora into the system, which among other things
would be interesting from the point of view of comparing various corpora. Currently a long-term
corpus development project is in progress at the National Institute of the Japanese Language(Maekawa 2006). Loading these corpora into the Sketch Engine tool is being considered (Tono2007). We will also consider possible beneﬁts of implementing some other morphological andstructural analysers for the Japanese language. Finally, we shall explore a direct applicationof the system to the creation of learner’s dictionaries (Erjavec et al. 2006) and CALL systems(Nishina and Yoshihashi 2007).

Acknowledgment
The authors would like to thank Serge Sharoﬀ for providing the URL list, which served as thebasis for constructing the JpWaC corpus, all the collaborators of the WAC project for making
their software available, and the anonymous reviewers for their useful comments.

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Irena Srdanović Erjavec: received the Bachelor degree in Japanese Language
from University of Belgrade in 1997, and Master degree in Linguistics from
Univesity of Ljubljana in 2005. Since April 2007 she has been a PhD student
at Human System Science Department at Tokyo Institute of Technology. From1997 to 2001, she worked as Japanese language advisor and technical writerat Hermes SoftLab in Ljubljana, from 2001 to 2002, as a translator and international trainee coordinator at Pioneer Corporation in Tokyo, and from 2005
to 2006 as a teacher assistant for Japanese Language at University of Ljubljana. Her research interests lie in the ﬁelds of corpus linguistics, lexicography
and Japanese language education, particulary concentrating on application ofhuman language technologies.
Tomaž Erjavec: received his BSc (1984), MSc (1990), and PhD (1997) degrees
in Computer Science from the University of Ljubljana; he also received an
M.Sc. in Cognitive Science (1992) from the University of Edinburgh. Heworks as a scientiﬁc associate at the Dept. of Knowledge Technologies at the
research Institute Jožef Stefan, with teaching positions at several universities.
He has been a visiting researcher at the University of Edinburgh, Univesityof Tokyo and the Joint Research Center of the European Commission. His
research interests lie in the ﬁelds of computational linguistics and language
technologies, with a large part of the work devoted to developing Slovene andmultilingual language resources. He has served two terms on the Board of the
EACL, has been a member of the Text Encoding Initiative Council and was
the founding president of the Slovenian Language Technologies Society. See
also http://nl.ijs.si/et/
Adam Kilgarriﬀ : is Director of Lexical Computing Ltd. which has developed
the Sketch Engine http://www.sketchengine.co.uk, a leading tool for corpus
research. His scientiﬁc interests lie at the intersection of computational linguistics, corpus linguistics, and dictionary-making. Following a PhD on “Polysemy” from Sussex University, he has worked at Longman Dictionaries, Oxford
University Press, and the University of Brighton, and is now Director of the
Lexicography MasterClass (http://www.lexmasterclass.com) as well as Lexical
Computing Ltd. He is Visiting Research Fellow at the Universities of Leeds andSussex. He started the SENSEVAL initiative on automatic word sense disambiguation and is now active in moves to make the web available as a linguists’22

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A web corpus and word sketches for Japanese

corpus. He is the founding chair of ACL-SIGWAC (Association for Computational Linguistics Special Interest Group on Web as Corpus) and has beenchair of ACL-SIG on the lexicon and Board member of EURALEX (European
Association for Lexicography). See also http://www.kilgarriﬀ.co.uk/(Received July 3, 2007)(Revised October 23, 2007)(Accepted November 15, 2007)23