Corpus-based Extraction of Japanese Compound Verbs
James Breen and Timothy Baldwin
Department of Computer Science & Software Engineering
University of Melbourne
Australia
jimbreen@gmail.com, tb@ldwin.net

Abstract
We describe two methods for Japanesecompound verb (JCV) extraction, based onsynthesis and pattern matching over the
Google Japanese n-gram corpus. We devise a number of filters to boost the precision of the corpus-based method, and evaluate the two methods based on a sample
of JCVs occurring in varying frequency
bands. We also investigate the distributionof JCV token frequency, and the type frequency of their components.

1 Introduction
This paper describes work conducted in a projectto extract Japanese compound verbs (JCVs) from
corpora and corpus-based resources. Compoundverbs in Japanese have attracted considerable
attention in Japanese linguistics as they are ahighly productive and flexible element of the language (Shibatani, 1990; Baldwin and Bond, 2002;
Tsujimura, 2006). Apart from some manuallyprepared verb lists they have received relatively
little attention in corpus linguistics.
The reasons for collecting and studying
Japanese compound verbs include:a. the development of reliable methods for extraction of the verbs from corpora;b. investigation of the distribution of the verbs
and their constituents;
c. investigation of the coverage of the verbs inthe major lexicons

In particular, it is hoped that by isolating JCVs
which are in use, but are not currently recorded
or lexicalized, and eventually by developing andverifying Japanese meanings and English translational equivalents for these verbs, the lexicon of
JCVs can be expanded.
At the current stage of the project, two methods for extracting compound verbs have been developed and applied over a major Japanese corpus. The result has been the identification of alarge number of potential JCVs, which we show
to have a high level of precision, relative to a sample of JCVs across varying frequency bands. We
also investigate the distribution of the frequencyof the verbs and their components.

2 Overview of Japanese CompoundVerbs
The compound verb in Japanese (複 合 動 詞
fukugôdôshi, hereafter JCV) is a concatenationof two or more verbs which function as a single multiword verb. There are several classes of
JCV, however in this work we concentrate of thelargest and most common class in which the firstverb is in the continuative form (also known as themasu-stem because it forms the base for the polite
spoken -masu group of inflections) (Uchiyama etal., 2005; Kubota, 1992).1 In common with moststudies of JCVs, we concentrate on verbs where
both components are native Japanese verbs, notloanwords or Sino-Japanese words. This exclusion is because these latter verbs are much less1
In some cases this undergoes phonetic variation, e.g. thegemination in 引っ越す hikkosu as an alternative to the regular 引き越す hikikosu.

common and have a different morphology.
As a JCV consists of two adjacent verb components, we will refer to these components as the V1
and V2. A typical JCV is 行き過ぎる ikisugiru“to go too far”, where the V1 is the continuative
form of 行く iku “to go” and the V2 is 過ぎる
sugiru “to be excessive; to be too much”. 過ぎる
is a particularly productive V2. A less productive
V2 is 汚す yogosu “to make dirty”, found in the
JCV 食べ汚す tabeyogosu “to eat messily”.
JCVs play a role in Japanese which is analogous to several different structures in other languages. English equivalents include compoundverbs (e.g. to start to eat), verb-plus-gerund (e.g.
to start swimming) and verb particle constructions(e.g. to kick up (ball, fuss), to pull down).
The JCV is a highly productive form, withsome particular V1s and V2s being strongly represented, however there is no real restriction ona verb being used within a JCV, subject to issues
such as aspect and valency (Kubota, 1992), andthe result being meaningful. Some popular references list many hundreds of JCVs (Tagashira andHoff, 1986) and major dictionaries typically include several thousand as entries, however it isgenerally recognized that many more JCVs are in
use than are lexicalized. The incompleteness of
the lexicalization of JCVs arises not only fromtheir productivity, but from the fact that their
meaning is often obvious to a Japanese speaker,
and hence dictionary editors usually concentrateon JCVs which are polysemous, or have idiosyncratic meanings. Extension of the coverage ofrecorded and translated JCVs would be of assistance in areas such as language learning, and in
lexicons used by morphological analysis and machine translation systems.
An example of a polysemous JCV is 引き抜く
hikinuku, from 引く hiku “to draw; to pull”, and抜く nuku “to extract”. It means both “to uproot”
and “to pull out”, and less obviously “to headhunt” and “to lure away”.

3 General Approach and Resources
3.1 Approaches
A fundamental problem with searching Japanese
corpora for unrecorded words is that Japanese text
does not usually have spaces or any other mark-JMdictKanjidic

V1 & V2s
JCV Model
Synth. JCVs

N−gram Corpus

JCV List

Filters

JCV List

Figure 1: An outline of the two proposed approaches
for JCV generation/extraction

ing between words. Thus the identification ofwords in text necessitates the use of a morphological analysis process to separate the words,
and all such processes currently rely on extensive
lexicons. The absence of a word in the lexicon
usually results in the analysis software defaulting
to producing a sequence of untagged morphemes
until it can resynchronize.
An approach that has had some previous success is to synthesize possible words by mimicking
Japanese morphological processes, and then testing, e.g. using a WWW search engine, to determine whether the word is in use (Breen, 2004a).
A variant of this approach has been applied in
the current project. A second approach in which
the Google n-gram corpus (described below) was
scanned using a filter designed to detect the character patterns consistent with JCVs. These approaches are described in detail below. Figure 1
shows a diagram of the two approaches.
3.2 Resources Used
The project uses several lexical resources toassist with the identification and extraction of
JCVs. The JMdict Japanese-English dictionary
database (Breen, 2004b) and the associated Kanjidic database (Breen, 2009) were used to establish sets of possible V1 and V2 components, anda combined lexicon was constructed from the JMdict file and the following:• the Kôjien Japanese dictionary (Ootsuka,
1998)• the Daijirin Japanese dictionary (Matsumura, 1995)• the GoiTaikei lexicon (Ikehara et al., 1999)• the Japanese Linguistics Database (JLD)(Halpern, 2008)
The Kôjien and Daijirin are major Japanesedictionaries with extensive coverage of theJapanese lexicon. The GoiTaikei and JLD commercial lexicons are primarily used for Japanese
NLP projects and research. The combined lexicon
contains over 650,000 surface forms of Japanesewords.2
The main corpus used in this project has been
the Google Japanese n-grams (Kudo and Kazawa,
2007). The set of n-grams in the corpus was compiled by extracting text from a complete crawlof Japanese WWW pages for the month of July,
2007, and analyzing that text using the MeCabmorphological analysis system (Kudo, 2008). ngram sequences of up to 7-grams are recorded in
the corpus if they were identified in 20 or more
text segments. As systems such as MeCab are observed to break JCVs into up to three morphemes
depending on the inflection of the verb, only the
1-gram, 2-gram and 3-gram section of the corpus
were used in this study, and then only those ngrams which began with a kanji character.
A similar Google n-gram corpus has been usedsuccessfully in the extraction of verb-particle constrictions in English (Kummerfeld and Curran,2008).
2

Japanese is written using a combination of Chinesecharacters (kanji) and two syllabaries: hiragana andkatakana. It has considerable flexibility as to whether words
are written in kanji, one of the syllabaries, or a mixture.
Also, alternative kanji are often used. For example, theJCV 詰め合わせる tsume-awaseru “to pack an assortmentof goods, etc.” can also be written: 詰め合せる, 詰合わせる, 詰合せる, 詰めあわせる or つめあわせる, and 並び変える narabikaeru “to put things in order” can also be written
並変える, 並び替える, 並替える, etc..

3.3 Synthesis of Compound Verbs
In this approach, a set of JCVs were synthesized
as follows:a. The JMdict lexicon was examined and JCVs
identified.
Including alternative surface
forms, some 2,900 JCVs in which kanji wereused in both the V1 and v2 were extracted.
These were divided into the V1 and V2 components, yielding approximately 700 V1s
and 600 V2s.
b. Using the V1 and V2 components, 420,000
synthetic JCVs were created via all combinations of the V1s and V2s. For each verb two
forms were generated: the form in which theV2 used kanji as the root of the verb, andthe form in which the V2 was entirely in the
hiragana script. Both these forms are freely
used in Japanese, for example 抱き付く dakitsuku “to cling to; to embrace” can equallywell be written 抱きつく, and in fact the latter is more commonly used.
For each of these, as well as the plain nonpast tense (which is considered to be the reference form of Japanese verbs and is used
for dictionary headwords) two inflections
were generated: the continuative te-form andthe plain past tense. These three are themost commonly used inflections in written
Japanese, and it was considered appropriate
to focus on them in order to detect whether
words were in use.
Each synthetic JCV was initially checkedagainst the combined lexicon, resulting in a totalof 6,094 matches.
Each synthetic JCV was then checked against
the Google n-gram corpus. As there were three
inflections of two written forms of 420,000 JCVs,
a total of 2,520,000 words were tested. The sections of the n-gram files which began with a kanji
were preprocessed to recombine each 2-gram and3-gram into a single character string, then sorted,
resulting in a file of 270M unigrams in a file of
5.8GB. This facilitated processing in a single passagainst the sorted verb file, thus enabling a rapid
comparison and collation of results.
Initially, approximately 26,000 of the synthesized JCVs were matched in one or more of theirinflections. On inspection, the JCV form in which
the V2 was in hiragana did not contribute significantly to the matches, and as this form has an increased chance of homophones which cannot be
resolved without textual context, it was removed
from the analysis. Also removed were a numberof JCVs which were effectively alternative conjugations (passive, potential, etc.). This reduced thematched JCVs to 22,692.
Of the 6,094 JCVs which were found in the
combined lexicon, 4,779 matched n-grams in the
corpus, i.e. 1,315 which were found in the combined lexicon were not in the corpus. On inspection it was noted that many of the 1,315 were archaic and literary words.
The distribution of the counts of occurrencesin the corpus is sharply asymptotic, with a small
number having very high counts and declining toa long tail with over 15,000 having counts below
500.
3.4 Direct n-gram Search
In addition to the synthesis approach, an alternative approach was devised in which the n-gram
corpus was scanned for character strings which
conformed to the structural pattern of JCVs. Fromthe examination of known JCVs, it can be determined that the common structural pattern is:• a V1 consisting of one or two kanji followed
by one to three hiragana;• a V2 consisting of one or two kanji followed
by one to four hiragana.
As there are many other valid text fragments which also conform to this pattern, e.g.
noun/particle/verb, noun/particle/adjective, etc.
filters were applied as follows:a. the V1 component was limited to the masustems of known or potential verbs. To dothis, a list of verb masu-stems was createdand merged from:i. all the verbs in the JMdict dictionary;ii. all the V1 components used in the synthesis methods;iii. all the kanji in the Kanjidic databasewhich had the potential to form averb (this information is detailed in the
database.)

A total of 6,023 actual or potential verb
stems were identified and used to filter the
potential JCVs.
b. the inflecting part of the V2 was limited tothe hiragana strings associated with valid
verbs in the plain non-past, plain past andte-form inflections. A list of 208 such inflections was compiled and used as a filter.
A scan of the n-gram corpus for unigrams
which conformed to the structural model andpassed the filters yielded just on 135,000 potential
JCVs. As these included many inflected forms, aconsiderable amount of post-processing was carried out to reduce them to a consolidated set ofreference (plain non-past) forms. Some of theprocesses involved included:
a. matching the inflected and reference forms
and combining the counts;
b. detecting and removing additional inflections such as the potential and passive forms,
which share some of the inflection patternsof the reference form;c. detecting and removing adjectives.
In
Japanese, adjectives inflect in a manner similar to verbs, and a number had been collectedin the scan.
From this a reduced list of approximately
80,000 potential JCVs was produced. Whentested against the combined lexicon, 6,203
matched with one or more of the dictionaries, anincrease of 1,424 over the synthesis method. It
is clear that this approach has an improved recall, i.e. the number of actual JCVs identified as aproportion to the number in existence, relative tothe published lexicons, but possibly at the priceof a reduced precision, i.e the proportion of actual JCVs among the potential JCVs. As with thesynthesized JCVs, the distribution of the counts is
asymptotic with a long tail.

4 Analysis of the Potential CompoundVerbs
A detailed comparison of the potential JCVs compiled in the two approaches revealed that all thesynthesized JCVs which had matched unigrams

Compound Verbs: Token Counts and Dictionary Matches
1e+08

100
Token Counts
Dictionary Matches (%)

1e+07
80

60

10000010000

40Dictionary Matches (%)

Token Counts

1e+06

1000
20
100

10
0

10000

20000

30000

40000

50000

60000

70000

0
80000

Figure 2: Analysis of the token counts and dictionary matches of JCVs, ranking in decreasing order of token
frequency

in the corpus has also been collected in the search,and moreover the n-gram counts were almost always identical. This meant that a combined set
could be used for further analysis, with tagging
as to whether a JCV had been detected by bothmethods, or by the search alone.
Figure 2 shows the distribution of the n-gramcounts and also the proportion of the potential
JCVs which were found in a lexicon.3
A key issue is the extent to which these methods have revealed actual JCVs as opposed tocharacter sequences which simply share symbolic
characteristics with JCVs. To examine this aspectfully would require an evaluation of each JCV
candidate in context, e.g. as it is used in WWW
texts, to determine its status. In order to estimate the effectiveness of the JCV extraction approaches, samples of 50 potential JCVs were selected at random from each of three bands based
on token frequency
a. High: JCVs with over 5,000 counts in the ngram corpus (3,795 JCVs)
b. Medium: JCVs with 1,000 to 4,999 counts
3
For the purposes of depicting this, JCVs were examinedin batches of 500, and the percentage which matched wereplotted.

(4,886 JCVs)
c. Low: JCVs with 20 to 999 counts (71,138
JCVs)
The sample JCV candidates were classified as
to whether they were in a lexicon or not, and if
not, whether they were actually verbs. For thelatter analysis, each potential JCV was manuallychecked against WWW pages via a search engine to verify whether it was being used as a verb.
(At some later stage it may be possible to employ
deeper linguistic analysis to carry out this process
automatically.)
The summary of this classification is in Table 1.
The figures in parentheses are numbers of JCVs ineach category resulting from the search approach
alone.
The JCV candidates which were classified as“other”, i.e. not verbs, fell into several categories. The most common were inflected adjectives which had not been detected in filtering, adverbs such as 再び, V1s such as 見て which probably should have been filtered out (see below),
and apparent typographical or grammatical errors
associated with other verbs. Some were otherconstructs such as noun/verb without the usual intervening particle.

In lexicon
Not in lexicon
verb
other

High
27 (3)
23 (14)7 (1)16 (13)Med
12 (2)
38 (20)26 (8)
12 (12)Low
1 (0)49 (35)27 (14)22 (21)

Table 1: Analysis of sample JCV candidates over thethree frequency distribution bands, in terms of theiroccurrence in the lexicon; for JCV candidates not in
the lexicon, we additionally break down the counts
into verb and non-verb candidates
JMdictKôjien
Daijirin
GoiTaikei
JLD

High
1,788 (0.47)
1,420 (0.37)
1,626 (0.43)
1,375 (0.36)
2,172 (0.57)

Med
453 (0.09)
401 (0.08)
491 (0.10)377 (0.08)
932 (0.19)

Low
671 (0.01)
976 (0.01)
970 (0.01)
661 (0.01)
2,023 (0.03)

Total
2,912
2,797
3,087
2,413
5,126

Table 2: Occurrence of potential JCVs in the different
dictionaries across the three frequency bands, in termsof the raw type count and proportion of overall types(in parentheses)

Of considerable interest is the relative performance of the JCVs identified by the synthesis approach; in each of the three bands almost all ofthese JCVs were valid verbs.
In terms of the precision of the different approaches, the full set of potential JCVs achieved
precisions of 0.68, 0.76 and 0.56 respectively
across the three selected bands, however within
this the synthesis approach achieved precisions of
0.91, 1.00 and 0.93.
The comparative recall is difficult to measure
as there is no gold standard for the number of
JCVs in use or able to be used. Certainly the direct search approach achieved a greater recall butat the price of a lower precision.
As reported above, 6,203 of the potential JCVs
matched with one or more of the dictionaries
which make up the combined lexicon. It wasnoticed that while the high-ranking JCVs tended
to match all the dictionaries, lower-ranking JCVs
tended to match more sparsely, with often only
one or two dictionaries matching. The specificdictionary matches were extracted for each of thehigh, medium and low bands. These are shown in
Table 2. The figures in parentheses are the proportions of the dictionary matches against the total dictionary matches for the band.

V1
V2

Combinedn-gram2,601
8,883

Syntheticn-gram680
591

Lexicon
1,294
1,314

First
10,0001,290
1,597

Table 3: V1 and V2 frequencies for the two proposed
methods and in the lexicon

5 Productivity Measures of V1 and V2
Components
The productivity of the JCV is well known, as is
the frequency with which some V1 and V2 components appear. It is useful, having established areasonably large collection of JCVs, to use thisto analyze the frequency of usage of the components.
For the purpose of this analysis, the V1s andV2s were extracted, counted and ranked from:• the full collection of possible JCVs collectedfrom the n-gram corpus;• the synthesized JCVs which had matches in
the n-gram corpus;• the JCVs extracted from the combined lexicon;• the highest-ranked 10,000 JCVs in the fullcollection (to see if there is a bias in component use according to the how common the
JCV is).
In addition, V2 rankings collected by Kubota(1992) from her own corpus analysis and froman earlier published collection (Nomura and Ishii,1987) were added for comparison.
The number of V1s and V2s which were extracted are shown in Table 3. As can be seen,
over 80% of the V2s in the combined file are only
found in the relatively low-frequency JCVs.
While there was some correlation of the frequency rankings of the V1s and V2s, there werealso some notable differences. This can be seen in
Tables 4 and 5, which show the 20 most common
components as they were found in the combined
JCV list, with their comparative rankings in theother lists.
With regard to the V1s in Table 4, it will be
noted that there are some which do not appear in

V1
通り
再び
思い
見て
限り
同じ
入り
余り
使い
出て
取り
入れ
行き
買い
持ち
作り
考え
言い
及び
感じ

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

Synth. n-gram
2
1
3
4
–
27
13
–
11
7
5
19
16
24
22
15
17
9
–
30

Lexicon
190
642
11
520
857
–
52
726
45
823
1
49
17
24
25
68
113
4
1242
428

First 10,000
1
3
2
4
9
51
29
18
13
37
5
24
22
11
19
30
25
10
132
41

Table 4: V1 rankings for the two proposed methods, the lexicon and the most frequent 10,000 JCVs

V2
始める
続ける
過ぎる
出す
合う
得る
行く
初める
終わる
切る
難く
込む
多く
直す
見る
忘れる
上げる
歩く
来る
頑張る

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

Synth. n-gram
1
2
4
3
5
13
10
8
7
11
–
6
–
9
14
15
12
19
22
–

Lexicon
6
3
4
2
5
152
59
106
74
9
–
1
–
10
109
18
8
50
291
–

First 10,000
1
2
5
3
6
11
12
43
7
10
28
4
56
9
27
16
8
25
31
68

Kubota
1
8
9
3
4
2
–
–
13
5
–
7
–
11
–
39
21
34
–
–

Table 5: V2 rankings for the two proposed methods, the lexicon and the most frequent 10,000 JCVs

all lists, or have very different rankings. Thesealmost all relate to differing interpretations at towhat comprises a JCV. For example:
a. the V1s 限り kagiri “restricted”, 余り amari“remain” and 及び oyobi “and”, while deriving from verbs, are almost invariably use
as conjunctions or adverbials in modern
Japanese, and hence would not normally bepart of a JCV. They should be added to thefilter rules.
b. 再び futatabi “again” is more commonly regarded as an adverb, and should also probably be excluded.
c. the two “te-form” V1s (見て mite and 出てdete) could be classed as either part of JCVs,
or as the common (V1te−form ,V2) sequencewhich has the sense of simultaneous occurrence or activity. They are often excluded
from JCV classifications.
It will be noted that few of the high-ranking
V1s in the lexicon appear in Table 4. On inspection it proved that they mostly lie in the 20–40
range in the n-gram lists. Given that dictionary
compilers concentrate on JCVs which are polysemous or have idiosynchratic meaning, a lack offrequency alignment with corpus-based lists is to
be expected.
While there is generally good agreement between the rankings of the lists of potential V2s
in Table 5, some attract similar comments:
a. two of the potential V2s (難く gataku and 多く ôku) are clearly derived from adjectives,and should be added to the filter rules.
b. V2s which rank lower in the lexicon list
(得る eru “to attain”, 初める hajimeru “tostart”, etc.) are usually parts of semanticallyregular JCVs, and hence are less likely to be
in a dictionary.
c. the appearance of 頑張る gaNbaru “to persist; to insist on; to stand firm” is of interest.
It is not usually regarded as a JCV component, yet from its occurrence in the n-gramlists, it is clearly being used as such.

The foregoing comments are confined to theV1 and V2 components appearing among the 20
highest ranking counts in the combined list; similar comments can be made about a number oflower-ranking components. There is scope forconsiderable analysis of the ranking lists of V1
and V2 components.

6 Conclusions and Future Work
The work so far in the project has demonstrated
that large numbers of JCVs are in regular use
and can be detected through the application of
NLP techniques to corpora. The two detection
techniques which have been developed and tested
have been demonstrated to have good levels of
precision, especially in the case of the JCV synthesis method.
A substantial list of JCVs which are notrecorded in commonly-used dictionaries has been
identified for further study. In addition data on thefrequency of usage of JCVs and their V1 and V2
components has been collected and can be madeavailable for other Japanese NLP projects.
Future work in the project will include the development and testing of the meanings of unrecorded JCVs. The approach developed in earlier work (Uchiyama et al., 2005), which employs
rule-based and statistical methods based on extensive classification of the V1 and V2 components,
will be followed.

7 Acknowledgment
The assistance of Mr Jack Halpern, who has
kindly provided a copy of his Japanese Linguistic Database for use in the project, is gratefully
acknowledged.

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