言語処理学会 第21回年次大会 発表論文集 (2015年3月)

A Japanese Word Dependency Corpus
Shinsuke Mori,1 Hideki Ogura,2 Tetsuro Sasada3
1,3Academic Center for Computing and Media Studies, Kyoto University
2
College of Letters, Ritsumeikan University

1Introduction

Empirical methodology in natural language processing (NLP) has experienced a great success recently[1]. In this methodology, language resource availability is the most important. In fact Penn Treebank [2], annotated with part-of-speech (POS) information and phrase structure, has driven various
researches on POS tagging models and parsing models. Therefore similar corpora have been developedin various languages.
For Japanese, the language we focus on this paper,a high quality balanced corpus called Balanced Corpus of Contemporary Written Japanese (BCCWJ)[3] has been issued recently. The sources of the corpus varies from newspaper articles to blogs. The sentences in the core part of this corpus (BCCWJ Coredata) are segmented into words, called a “short-unitword” and each word is annotated with a part-ofspeech (POS) and pronunciation. Using the BCCWJ as a training corpus, high accuracy has beenachieved in word segmentation [4], POS tagging [5],and pronunciation estimation [6]. This corpus, however, does not have other higher order phenomena.
Among these we focused on the dependency structure and we annotated more than 30 thousands
sentences including the BCCWJ Core data. For
Japanese there is a dependency corpus [7]. Its unit
is, however, phrase called bunsetsu. This Japanese
speciﬁc unit is not compatible with the word unit inother languages. Thus there is a strong requirementof a word-based dependency corpus.
In this background, ﬁrst we designed the standardof word dependency annotation for Japanese. The
word unit is compatible with BCCWJ. Next we annotated more than 30 thousand sentences with dependency structure. In this paper, we ﬁrst present
the speciﬁcation of our corpus. Then we give a detailed explanation about our standard of word dependency. We also report some experiments on dependency parsing using our corpus.

2Corpus Specification

In this section, we present the details of our word
dependency corpus, except for the dependency standard, which we discuss in the next section.

2.1

Unit Definition

For the dependency annotation unit, we have chosen the word as in many languages. As we noted in
the previous section, a language speciﬁc unit calledbunsetsu is famous for Japanese dependency description [7]. This unit is, however, too long for various

applications. In fact in some languages, a sentence
is separated into phrases by white spaces when it iswritten1 . But phrases are divided into some smaller
units in many researches [11]. From the above observation, we decided to take word as the unit of ourdependency corpus.
For the deﬁnition of word, we follow that of BCCWJ, which is a mature standard created by linguists of Japanese language. The only diﬀerence isthat we separate the endings of inﬂectional words(adjectives, verbs, and auxiliary verbs) from theirstems for two reasons.
1. By taking stems and endings into the vocabulary separately, we can build a higher coverage
language model (LM) with a smaller vocabulary. This allows us to increase the performanceof LM-based applications such as an automatic
speech recognizer (ASR) [12] and input method(IM) [13].
2. By separating endings from stems, we can identify diﬀerent inﬂection forms of the same verb
just by a string match2 . That is, we do notneed to prepare the list of inﬂection patterns
and the correct inﬂection pattern at the step ofmorphological analysis as well.

2.2

Source and Size

Some experimental results [14] demonstrate that theparsing accuracy is high enough for real applications
if a high quality dependency corpus is available in
the application domain. Now the focus of parsing
research has been shifting to domain adaptability ofmethods. Therefore, we decided to take sentencesfrom various domains to allow corpus users to conduct domain adaptation experiments. Table 1 shows
speciﬁcations of our corpus. Each word, except for
the root word, is annotated with its head (dependency destination). Thus the number of dependencies in a corpus is equal to the number of words minus
the number of sentences.
Below we explain the features of each domain andthe reason why we have chosen them.
2.2.1 BCCWJ Core data
BCCWJ [3] has a core part whose sentences are manually segmented into words and the words are annotated with their POS and pronunciation. The an1 In many researches on these languages, these phrases arecalled word because of they are visually similar to English
word but they are phrase in granularity of meaning.
2 Some words such as “行 く” (go) and “行 う” (execute)share the stem (“行” in these examples). This ambiguity may
be resolved by a method for word sense disambiguation.

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Table 1: Corpus speciﬁcations.
IDsource#Sentences #Words #Characters
Yahoo! questions and answers
615
12,487
17,294White papers
658
26,546
38,847
Yahoo! blog
857
13,386
19,833
BCCWJ
Books
1,058
23,47332,356
Magazines
1,505
25,274
39,842
Newspaper articles
1,713
38,06355,454
subtotal
6,406
139,229
203,626
EHJ
Dictionary example sentences
13,000162,273
220,148
NKN
Economy newspaper articles
10,025292,253442,264NPTNTCIR patent disclosure
500
20,65332,139total
29,931
614,408898,177
For the latest speciﬁcations see http://plata.ar.media.kyoto-u.ac.jp/tool/EDA/model.html .
OCOWOYPBPMPN

notation quality is very high and the accuracies ofPOS tagging and that of pronunciation estimation
are both more than 98%.
We annotated 1/10 of this part with word dependency. These data allow NLP researchers to work on
joint models for POS tagging and dependency parsing [15, 9] and structured language models [16, 17] for
automatic speech recognition [12] or input methods[13]. A research on the inﬂuence of syntactic structure to the pronunciation is also interesting since thepronunciation estimation of some important words
can only be solved by referring to long dependencies.
Some researchers are annotating BCCWJ Core
data about other linguistic phenomena including
predicate-argument structure, coreference, etc. With
our dependency annotation, various researches areexpected to be possible.
2.2.2 Dictionary example sentences: EHJ
We annotated about 80% sentences of the example sentences in a dictionary for daily conversation[18]. There are two important features. The ﬁrst
one is that this set covers the basic vocabulary inJapanese consisting of about 2,500 words in various
basic meanings. Our dependency annotation on thisset is useful to build a parser for spoken Japanese.
The second feature is that each Japanese sentence
has its English translation3 , which is useful for machine translation (MT) experiments.
The sentences have word boundary information ofcourse. And words are annotated with their pronunciation but not with POS tag. We conducted an experiment of automatic word segmentation and POS
tagging. The result showed that a publicly available
state-of-the-art POS tagger KyTea [5] trained on BCCWJ achieved about 98% accuracy on a small subsetof these sentences.
2.2.3 Economy newspaper articles: NKN
Penn Treebank [2] consists of sentences in Wall Street
Journal, which is a newspaper for economy. So we
focused on a newspaper specialized in economy. In
3 The French and German translation is also available inprinted version but not in machine readable form.

Japanese Nikkei newspaper is the only clear counterpart of Wall Street Journal. We annotated the sentences taken from this newspaper with word boundary information and dependency structure. This allows researchers to compare Japanese and English.
BCCWJ has a subset taken from articles of general
newspapers (PN in Table 1). However, Table 1 indicates that the average sentence length of this Nikkei
set is 29.2 words which is much larger than that ofBCCWJ PN, the second longest set (22.2 words).
Similar to EHJ, words are annotated with theirpronunciation but not with POS tag. An experimentof word segmentation and POS tagging in the samesetting as the EHJ case showed that the accuracy isabout 96%.
2.2.4 Invention disclosures: NPT
NTCIR deploys a shared task for patent machinetranslation [10] and makes English-Japanese sentence pairs taken from invention disclosures publicly
available. We annotated a small part of this set withword boundary information and dependency structure.
With this set we can adapt a dependency parser
to the patent domain and measure the parsing accuracy. Then MT researchers can use that parser
to automatically annotate invention disclosure sentences with dependency structure and work on treebased machine translation.

3Annotation Standard

The dependency annotation standard of our corpus
is basically similar to that of other treebanks. That
is to say, a source word ws depends on another word
wh , called a head, that the word modiﬁes and theconcatenation of the source word and the head ws whshould be a natural word sequence which may appear
in a huge corpus. Figure 1 shows an example. In thissection we present regulations for frequent phenomena taken from our annotation guideline.

3.1

Simple sentence

Basically Japanese is an SOV language. That is tosay, the word order in a simple sentence is subject,object, and verb. Almost all noun phrases have a― 510 ―

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word-based dependency corpus
ID head word/POS
intra-party
01 02 党内/noun ?
02 03の/part. ?
of
03 04
議論/noun ?
discussion
04 19
は /part.
subj.
“05 15「 /symbol
06 08
保守/nounconservative
07 08
二/noun ?
two党/suﬀ. ?
party
08 09
09 10
論/noun ?
discussion
10 13
は /part.
subj.
good
11 12
よろし /adj. ?
12 13
く/inﬂ. ?
infl.
not13 14な/adj. ?
14 15
い/inﬂ. ?
infl.
15 16」/symbol ?
”16 17と /part. ?
thatsaying
17 18い/verb ?
18 19
う/inﬂ. ?
infl.
19 20
もの/noun ?things
20 21
だ /aux. ? is21 –。/symbol .
Figure 1: An example of dependencies for a sentence.
case marker called postposition to clarify its role tothe verb. The only limitation is to put the main verb
phrase at the end. That is to say, subject (subj.), direct object (d-obj.), indirect object (i-obj.), and otherverb modiﬁer such as adverbial phrases are orderedfreely.
In our corpus, the head of a noun phrase wn depends on its postposition wp , and wp depends on theverb wv as shown in the example below.
I

私

3.2?

?

?

?
?
?

?

を返す

subj. he i-obj. book d-obj. return infl.

は彼に本?

.

。

Compound wordthat huge language resource subj.

言語

資源

subj.

is

3.4

that say infl.

.

Coordination

A coordination structure is also a frequent phenomenon. In a coordination structure two or morephrases are concatenated by using a coordinationmarker. In Japanese the most frequent marker is“と /and.” This marker is similar to “and” in English but we put one at each point between elements
as follows.
?
? ?
? ?
andはIn this example “huge” depends on “resource” because what is “huge” is not “language” but “resource.” Another modiﬁer, “that,” depends on thehead of the noun phrase, “resource,” and it depends
on the following postposition.

3.3

We decided that the case marker “は /subj.” depends
on N2 , not on the auxiliary verb “だ /is.” The reason
is that an auxiliary verb can be omitted especially ina that-clause or sentence coordination. The head of
the case marker is always N2 independent from theexistence of an auxiliary verb.
) ? ? ? ?
? ?(N1 は N2だと 言 う 。
This is somewhat debatable because this breaks
the structural compatibility with many European
languages and makes the tree-based machine translation complicated.

We annotate a compound word with the structure
representing its meaning. Modiﬁers of a compound
word depend on its head (in many cases with very
few exceptions which modiﬁes a part of a compoundword) and there is only one dependency arc goingout from the head.
Let us take a noun phrase example, “huge language resource.”
?
?
?
?
その 巨大

N1 は /subj. N2 だ /is4

CopulaSome sentences have a copular verb. Most copula
sentences fall into the following type.

andd-obj.

N1 と N2 と N3 をIn this case, our annotation standard states that N1and N2 depend on each marker following them. Themarkers depends on the last element N3 , not on thenext element.

Parsing Experiments

The most typical usage of our corpus is to build aparser. In this section, we present parsing experiment results on our corpus.

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References

Table 2: Parsing accuracy.
ID

BCCWJ

EHJ
NKN
NPTOC
OW
OY
PB
PMPN

#Sentences
Training Test
365
250
408
250
607
250
808
250
1,255
250
1,463
250
11,700 1,300
9,023 1,002
450
50Accuracy
95.11%
91.27%
89.63%
94.14%
95.80%
92.66%
97.07%
93.22%
90.92%The parser we used is MST-based dependency
parser EDA4 [19]. We divided all the subset intoa training and a test part (see Table 2). Then webuild a single model of EDA from all the training
sets and measured the word-based accuracy on eachtest set.
From the results shown in Table 2, it can be said
that the easiest is the set of dictionary example sentences (EHJ). Magazines (BCCWJ PM) and Yahoo!
questions and answers (BCCWJ OC) are the second easiest. The reason may be their limitation on
the vocabulary and sentence pattern variations. The
most diﬃcult is the blog domain (BCCWJ OY). Thisset is composed of user generated contents (UGC)
and its topic varies widely. The invention disclosureset (NPT) is also diﬃcult. The sentences tend tobe long and the writing style is diﬀerent. There is,however, a clear application for this set, which is treebased machine translation. We need more training
data to increase the accuracy in these domains.

5Conclusion

In this paper, we reported the details of our wordbased dependency corpus in Japanese. The unit iscompatible with the Balanced Corpus of Contemporary Written Japanese (BCCWJ), which is of highquality and widely used for various NLP tasks. The
size of our corpus is about 30 thousand sentences,which is enough to train statistical parsers for thegeneral domain. We then discussed the dependency
annotation standard, and ﬁnally reported some preliminary results of an MST-based dependency parser
on our corpus.

Acknowledgments
This work was supported by JSPS Grants-in-Aid for
Scientiﬁc Research Grant Numbers 23500177, NTTagreement dated 05/23/2013, and Basic Research onCorpus Annotation project of The National Institutefor Japanese Language and Linguistics. We are also
grateful to the annotators for their contribution tothe design of the guidelines and the annotation eﬀort.
4 http://plata.ar.media.kyoto-u.ac.jp/tool/EDA/
home en.html (accessed on 2014/Feb./01).

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