Dependency Parsing of Japanese Spoken Monologue
Based on Clause Boundaries
Tomohiro Ohno†a) Shigeki Matsubara‡ Hideki Kashioka§
Takehiko Maruyama] and Yasuyoshi Inagaki\†Graduate School of Information Science, Nagoya University, Japan‡Information Technology Center, Nagoya University, Japan§ATR Spoken Language Communication Research Laboratories, Japan]The National Institute for Japanese Language, Japan\Faculty of Information Science and Technology, Aichi Prefectural University, Japana)ohno@el.itc.nagoya-u.ac.jp
Abstract
Spoken monologues feature greater sentence length and structural complexity
than do spoken dialogues. To achieve highparsing performance for spoken monologues, it could prove effective to simplify the structure by dividing a sentence
into suitable language units. This paper
proposes a method for dependency parsing of Japanese monologues based on sentence segmentation. In this method, thedependency parsing is executed in twostages: at the clause level and the sentence level. First, the dependencies withina clause are identified by dividing a sentence into clauses and executing stochastic
dependency parsing for each clause. Next,
the dependencies over clause boundaries
are identified stochastically, and the dependency structure of the entire sentenceis thus completed. An experiment using
a spoken monologue corpus shows thismethod to be effective for efficient dependency parsing of Japanese monologue sentences.

1

Introduction

Recently, monologue data such as a lecture andcommentary by a professional have been considered as human valuable intellectual property andhave gathered attention. In applications, such as
automatic summarization, machine translation andso on, for using these monologue data as intellectual property effectively and efficiently, it isnecessary not only just to accumulate but also to
structure the monologue data. However, few attempts have been made to parse spoken mono-logues. Spontaneously spoken monologues include a lot of grammatically ill-formed linguistic
phenomena such as fillers, hesitations and selfrepairs. In order to robustly deal with their extragrammaticality, some techniques for parsing of dialogue sentences have been proposed (Core andSchubert, 1999; Delmonte, 2003; Ohno et al.,2005b). On the other hand, monologues also havethe characteristic feature that a sentence is generally longer and structurally more complicatedthan a sentence in dialogues which have been dealt
with by the previous researches. Therefore, fora monologue sentence the parsing time would increase and the parsing accuracy would decrease. It
is thought that more effective, high-performance
spoken monologue parsing could be achieved by
dividing a sentence into suitable language units for
simplicity.
This paper proposes a method for dependency
parsing of monologue sentences based on sentence segmentation. The method executes dependency parsing in two stages: at the clause level
and at the sentence level. First, a dependency relation from one bunsetsu1 to another within a clauseis identified by dividing a sentence into clauses
based on clause boundary detection and then executing stochastic dependency parsing for eachclause. Next, the dependency structure of the entire sentence is completed by identifying the dependencies over clause boundaries stochastically.
An experiment on monologue dependency parsing showed that the parsing time can be drasti1

A bunsetsu is the linguistic unit in Japanese that roughly
corresponds to a basic phrase in English. A bunsetsu consists of one independent word and more than zero ancillary
words. A dependency is a modification relation in which adependent bunsetsu depends on a head bunsetsu. That is, thedependent bunsetsu and the head bunsetsu work as modifier
and modifyee, respectively.

is presented in Fig. 1. This sentence consists offour clauses:
先日 総理府 発表いた 世論 より 死刑を 支持する 人が 八十パー なって
セント近く おりますが しました 調査に ますとというに

the other that the
day

The

indicates

capitalPrimepublicthatpunishment

Minister’sannounced

opinion

Officepoll

advocating

the ratio

nearlyof people

80%

is

The public opinion poll that the Prime Minister‘s Office announced the other day indicates thatthe ratio of people advocating capital punishment is nearly 80%：Dependency relation whose dependent bunsetsu is not the last bunsetsu of a clause：Dependency relation whose dependent bunsetsu is the last bunsetsu of a clause：Clause boundary：Clause：Bunsetsu

Figure 1: Relation between clause boundary anddependency structure• 先日総理府が発表いたしました (that thePrime Minister’s Office announced the otherday)• 世論調査によりますと (The public opinion
poll indicates that)• 死刑を支持するという (advocating capital
punishment)• 人が八十パーセント近くになっております(the ratio of people is nearly 80%)

cally shortened and the parsing accuracy can beincreased.
This paper is organized as follows: The next
section describes a parsing unit of Japanese monologue. Section 3 presents dependency parsing
based on clause boundaries. The parsing experiment and the discussion are reported in Sections
4 and 5, respectively. The related works are described in Section 6.

2

Parsing Unit of Japanese Monologues

Our method achieves an efficient parsing by adopting a shorter unit than a sentence as a parsing unit.
Since the search range of a dependency relation
can be narrowed by dividing a long monologuesentence into small units, we can expect the parsing time to be shortened.
2.1 Clauses and Dependencies
In Japanese, a clause basically contains one verb
phrase. Therefore, a complex sentence or a compound sentence contains one or more clauses.
Moreover, since a clause constitutes a syntactically sufficient and semantically meaningful language unit, it can be used as an alternative parsing
unit to a sentence.
Our proposed method assumes that a sentence
is a sequence of one or more clauses, and every
bunsetsu in a clause, except the final bunsetsu,
depends on another bunsetsu in the same clause.
As an example, the dependency structure of theJapanese sentence:
先日総理府が発表いたしました世論調査によりますと死刑を支持するという人が八十パーセ
ント近くになっております（The public opinion
poll that the Prime Minister’s Office announced
the other day indicates that the ratio of people
advocating capital punishment is nearly 80%)

Each clause forms a dependency structure (solid
arrows in Fig. 1), and a dependency relation fromthe final bunsetsu links the clause with anotherclause (dotted arrows in Fig. 1).
2.2 Clause Boundary Unit
In adopting a clause as an alternative parsing unit,
it is necessary to divide a monologue sentence
into clauses as the preprocessing for the following dependency parsing. However, since somekinds of clauses are embedded in main clauses,
it is fundamentally difficult to divide a monologue into clauses in one dimension (Kashioka andMaruyama, 2004).
Therefore, by using a clause boundary annotation program (Maruyama et al., 2004), we approximately achieve the clause segmentation ofa monologue sentence. This program can identify units corresponding to clauses by detecting
the end boundaries of clauses. Furthermore, theprogram can specify the positions and types ofclause boundaries simply from a local morphological analysis. That is, for a sentence morphologically analyzed by ChaSen (Matsumoto etal., 1999), the positions of clause boundaries are
identified and clause boundary labels are inserted
there. There exist 147 labels such as “compoundclause” and “adnominal clause.” 2
In our research, we adopt the unit sandwiched
between two clause boundaries detected by clauseboundary analysis, were called the clause boundary unit, as an alternative parsing unit. Here, we
regard the label name provided for the end boundary of a clause boundary unit as that unit’s type.
2
The labels include a few other constituents that do notstrictly represent clause boundaries but can be regarded as being syntactically independent elements, such as “topicalized
element,” “conjunctives,” “interjections,” and so on.

Table 1: 200 sentences in “Asu-Wo-Yomu”
sentencesclause boundary units
bunsetsus
morphemes
dependencies over clause boundaries

200
951
2,430
6,017
94

2.3 Relation between Clause Boundary Units
and Dependency Structures
To clarify the relation between clause boundary
units and dependency structures, we investigatedthe monologue corpus “Asu-Wo-Yomu 3 .” In theinvestigation, we used 200 sentences for whichmorphological analysis, bunsetsu segmentation,
clause boundary analysis, and dependency parsing were automatically performed and then modified by hand. Here, the specification of the partsof-speech is in accordance with that of the IPA
parts-of-speech used in the ChaSen morphological analyzer (Matsumoto et al., 1999), the rulesof the bunsetsu segmentation with those of CSJ(Maekawa et al., 2000), the rules of the clauseboundary analysis with those of Maruyama etal. (Maruyama et al., 2004), and the dependency
grammar with that of the Kyoto Corpus (Kurohashi and Nagao, 1997).
Table 1 shows the results of analyzing the 200
sentences. Among the 1,479 bunsetsus in the difference set between all bunsetsus (2,430) and thefinal bunsetsus (951) of clause boundary units,
only 94 bunsetsus depend on a bunsetsu locatedoutside the clause boundary unit. This resultmeans that 93.6% (1,385/1,479) of all dependency
relations are within a clause boundary unit. Therefore, the results confirmed that the assumption
made by our research is valid to some extent.

3

Dependency Parsing Based on Clause
Boundaries

In accordance with the assumption described in
Section 2, in our method, the transcribed sentenceon which morphological analysis, clause boundary detection, and bunsetsu segmentation are performed is considered the input 4 . The dependency
3
Asu-Wo-Yomu is a collection of transcriptions of a TVcommentary program of the Japan Broadcasting Corporation(NHK). The commentator speaks on some current social issue for 10 minutes.
4
It is difficult to preliminarily divide a monologue into
sentences because there are no clear sentence breaks in monologues. However, since some methods for detecting sentenceboundaries have already been proposed (Huang and Zweig,
2002; Shitaoka et al., 2004), we assume that they can be detected automatically before dependency parsing.

parsing is executed based on the following procedures:
1. Clause-level parsing: The internal dependency relations of clause boundary units are
identified for every clause boundary unit in
one sentence.
2. Sentence-level parsing: The dependency
relations in which the dependent unit is the final bunsetsu of the clause boundary units are
identified.
In this paper, we describe a sequence of clauseboundary units in a sentence as C1 · · · Cm , a sequence of bunsetsus in a clause boundary unit Ci
as bi1 · · · bini , a dependency relation in which thedependent bunsetsu is a bunsetsu bik as dep(bik ),and a dependency structure of a sentence as{dep(b11 ), · · · , dep(bmnm −1 )}.
First, our method parses the dependency structure {dep(bi1 ), · · · , dep(bini −1 )} within the clauseboundary unit whenever a clause boundary unit
Ci is inputted. Then, it parses the dependency
structure {dep(b1n1 ), · · · , dep(bm−1
nm−1 )}, which is aset of dependency relations whose dependent bunsetsu is the final bunsetsu of each clause boundary
unit in the input sentence. In addition, in both of
the above procedures, our method assumes the following three syntactic constraints:
1. No dependency is directed from right to left.
2. Dependencies don’t cross each other.
3. Each bunsetsu, except the final one in a sentence, depends on only one bunsetsu.
These constraints are usually used for Japanese dependency parsing.
3.1 Clause-level Dependency Parsing
Dependency parsing within a clause boundary
unit, when the sequence of bunsetsus in an input
clause boundary unit Ci is described as Bi (=bi1 · · · bini ), identifies the dependency structure
Si (= {dep(bi1 ), · · · , dep(bini −1 )}), which maximizes the conditional probability P (Si |Bi ). At
this level, the head bunsetsu of the final bunsetsu
bini of a clause boundary unit is not identified.
Assuming that each dependency is independentof the others, P (Si |Bi ) can be calculated as follows:
P (Si |Bi ) =

nY
i −1
k=1

rel

P (bik → bil |Bi ),

(1)

rel

where P (bik → bil |Bi ) is the probability that a bunsetsu bik depends on a bunsetsu bil when the sequence of bunsetsus Bi is provided. Unlike theconventional stochastic sentence-by-sentence dependency parsing method, in our method, Bi is
the sequence of bunsetsus that constitutes not asentence but a clause. The structure Si , which
maximizes the conditional probability P (Si |Bi ),is regarded as the dependency structure of Bi andcalculated by dynamic programming (DP).
rel
Next, we explain the calculation of P (bik →bil |Bi ). First, the basic form of independent words
in a dependent bunsetsu is represented by hik , its
parts-of-speech tik , and type of dependency rki ,while the basic form of the independent word ina head bunsetsu is represented by hil , and its partsof-speech til . Furthermore, the distance between
bunsetsus is described as dii
kl . Here, if a dependentbunsetsu has one or more ancillary words, the typeof dependency is the lexicon, part-of-speech andconjugated form of the rightmost ancillary word,
and if not so, it is the part-of-speech and conjugated form of the rightmost morpheme. The typeof dependency rki is the same attribute used inour stochastic method proposed for robust dependency parsing of spoken language dialogue (Ohnoet al., 2005b). Then dii
kl takes 1 or more than 1,that is, a binary value. Incidentally, the above
attributes are the same as those used by the conventional stochastic dependency parsing methods(Collins, 1996; Ratnaparkhi, 1997; Fujio and Matsumoto, 1998; Uchimoto et al., 1999; Charniak,
2000; Kudo and Matsumoto, 2002).
Additionally, we prepared the attribute eil to indicate whether bil is the final bunsetsu of a clauseboundary unit. Since we can consider a clauseboundary unit as a unit corresponding to a simple sentence, we can treat the final bunsetsu of aclause boundary unit as a sentence-end bunsetsu.
The attribute that indicates whether a head bunsetsu is a sentence-end bunsetsu has often beenused in conventional sentence-by-sentence parsing
methods (e.g. Uchimoto et al., 1999).
By using the above attributes, the conditionalrelprobability P (bik → bil |Bi ) is calculated as follows:rel

P (bik → bil |Bi )
∼
=
=

rel
i
P (bik → bil |hik , hil , tik , til , rki , dii
kl , el )reli
F (bik → bil , hik , hil , tik , til , rki , dii
kl , el ).
i
F (hik , hil , tik , til , rki , dii
kl , el )(2)Note that F is a co-occurrence frequency function.
In order to resolve the sparse data problems
relcaused by estimating P (bik → bil |Bi ) with formula(2), we adopted the smoothing method describedby Fujio and Matsumoto (Fujio and Matsumoto,i1998): if F (hik , hil , tik , til , rki , dii
kl , el ) in formula (2)rel

is 0, we estimate P (bik → bil |Bi ) by using formula
(3).
rel

P (bik → bil |Bi )
∼
=
=

(3)

rel
i
P (bik → bil |tik , til , rki , dii
kl , el )reli
F (bik → bil , tik , til , rki , dii
kl , el )i
F (tik , til , rki , dii
kl , el )3.2 Sentence-level Dependency Parsing
Here, the head bunsetsu of the final bunsetsu
of a clause boundary unit is identified. Let
B (= B1 · · · Bn ) be the sequence of bunsetsus of one sentence and Sf in be a set of dependency relations whose dependent bunsetsu isthe final bunsetsu of a clause boundary unit,{dep(b1n1 ), · · · , dep(bm−1
nm−1 )}; then Sf in , whichmakes P (Sf in |B) the maximum, is calculated by
DP. The P (Sf in |B) can be calculated as follows:

P (Sf in |B) =

m−1
Y

rel

P (bini → bjl |B),

(4)

i=1
rel

where P (bini → bjl |B) is the probability that abunsetsu bini depends on a bunsetsu bjl when thesequence of the sentence’s bunsetsus, B, is provided. Our method parses by giving consideration
to the dependency structures in each clause boundary unit, which were previously parsed. That is,
the method does not consider all bunsetsus located on the right-hand side as candidates for ahead bunsetsu but calculates only dependency relations within each clause boundary unit that do
not cross any other relation in previously parsed
dependency structures. In the case of Fig. 1,
the method calculates by assuming that only threebunsetsus “人が (the ratio of people),” or “なっております (is)” can be the head bunsetsu of thebunsetsu “指示するという (advocating).”rel
In addition, P (bini → bjl |B) is calculated as in
Eq. (5). Equation (5) uses all of the attributes usedin Eq. (2), in addition to the attribute sjl , whichindicates whether the head bunsetsu of bjl is the
final bunsetsu of a sentence. Here, we take into

400

Table 2: Size of experimental data set (Asu-WoYomu)
Parsing time [msec]

test data learning data
programs
8
95
500
5,532
sentencesclause boundary units
2,237
26,318bunsetsus
5,298
65,821
13,342
165,129
morphemes
Note that the commentator of each program is different.

350

our method
conv. method

300
250
200
150
100
50

Table 3: Experimental results on parsing time

rel

(5)

rel j
j j
ij
j
j
∼
= P (bini → bl |hini , hl , tini , tl , rni i , dni l , el , sl )
rel

=

4

j j
F (bini → bjl , hini , hjl , tini , tjl , rni i , dij
ni l , el , sl )j j
F (hini , hjl , tini , tjl , rni i , dij
ni l , el , sl )Parsing Experiment

To evaluate the effectiveness of our method forJapanese spoken monologue, we conducted an experiment on dependency parsing.
4.1

5
10
15
20
25
Length of sentence [number of bunsetsu]

30

Figure 2: Relation between sentence length andparsing time

account the analysis result that about 70% of the
final bunsetsus of clause boundary units depend on
the final bunsetsu of other clause boundary units 5
and also use the attribute ejl at this phase.
P (bini → bjl |B)00

our method conv. method
average time (msec)
10.9
51.9
programming language: LISP
computer used: Pentium4 2.4 GHz, Linux

Outline of Experiment

We used the spoken monologue corpus AsuWo-Yomu, annotated with information on morphological analysis, clause boundary detection,bunsetsu segmentation, and dependency analysis6 . Table 2 shows the data used for the experiment. We used 500 sentences as the test
data. Although our method assumes that a dependency relation does not cross clause boundaries,
there were 152 dependency relations that contradicted this assumption. This means that the dependency accuracy of our method is not over 96.8%(4,646/4,798). On the other hand, we used 5,532
sentences as the learning data.
To carry out comparative evaluation of our
method’s effectiveness, we executed parsing for5
We analyzed the 200 sentences described in Section 2.3
and confirmed 70.6% (522/751) of the final bunsetsus ofclause boundary units depended on the final bunsetsu of otherclause boundary units.
6Here, the specifications of these annotations are in accordance with those described in Section 2.3.

the above-mentioned data by the following two
methods and obtained, respectively, the parsing
time and parsing accuracy.
• Our method: First, our method provides
clause boundaries for a sequence of bunsetsus of an input sentence and identifies all
clause boundary units in a sentence by performing clause boundary analysis (CBAP)(Maruyama et al., 2004). After that, ourmethod executes the dependency parsing described in Section 3.
• Conventional method: This method parses
a sentence at one time without dividing it into
clause boundary units. Here, the probability
that a bunsetsu depends on another bunsetsu,
when the sequence of bunsetsus of a sentence
is provided, is calculated as in Eq. (5), where
the attribute e was eliminated. This conventional method has been implemented by us
based on the previous research (Fujio andMatsumoto, 1998).
4.2 Experimental Results
The parsing times of both methods are shown in
Table 3. The parsing speed of our method improves by about 5 times on average in comparison
with the conventional method. Here, the parsing
time of our method includes the time taken not
only for the dependency parsing but also for theclause boundary analysis. The average time taken
for clause boundary analysis was about 1.2 millisecond per sentence. Therefore, the time cost of
performing clause boundary analysis as a preprocessing of dependency parsing can be consideredsmall enough to disregard. Figure 2 shows the relation between sentence length and parsing time

Table 4: Experimental results on parsing accuracy
bunsetsu within a clause boundary unit (except final bunsetsu)
final bunsetsu of a clause boundary unit
total

Table 5: Experimental results on clause boundary
analysis (CBAP)
recallprecision

95.7% (2,140/2,237)
96.9% (2,140/2,209)

for both methods, and it is clear from this figurethat the parsing time of the conventional method
begins to rapidly increase when the length of asentence becomes 12 or more bunsetsus. In contrast, our method changes little in relation to parsing time. Here, since the sentences used in theexperiment are composed of 11.8 bunsetsus on average, this result shows that our method is suitablefor improving the parsing time of a monologuesentence whose length is longer than the average.
Table 4 shows the parsing accuracy of bothmethods. The first line of Table 4 shows theparsing accuracy for all bunsetsus within clauseboundary units except the final bunsetsus of theclause boundary units. The second line shows
the parsing accuracy for the final bunsetsus of
all clause boundary units except the sentence-end
bunsetsus. We confirmed that our method could
analyze with a higher accuracy than the conventional method. Here, Table 5 shows the accuracy of the clause boundary analysis executed by
CBAP. Since the precision and recall is high, we
can assume that the clause boundary analysis exerts almost no harmful influence on the following
dependency parsing.
As mentioned above, it is clear that our method
is more effective than the conventional method in
shortening parsing time and increasing parsing accuracy.

5

Discussions

Our method assumes that dependency relations
within a clause boundary unit do not cross clauseboundaries. Due to this assumption, the method
cannot correctly parse the dependency relations
over clause boundaries. However, the experimental results indicated that the accuracy of our
method was higher than that of the conventional
method.
In this section, we first discuss the effect of our
method on parsing accuracy, separately for bun-

our method
88.2% (2,701/3,061)
65.6% (1,140/1,737)
80.1% (3,841/4,798)

conv. method
84.7% (2,592/3,061)
63.3% (1,100/1,737)
76.9% (3,692/4,798)

Table 6: Comparison of parsing accuracy between
conventional method and our method (for bunsetsu
within a clause boundary unit except final bunsetsu)```
our method```
correct
conv. method ````
correct
incorrect
total

2,499
202
2,701

incorrect

total

93
267
360

2,592
469
3,061

setsus within clause boundary units (except the final bunsetsus) and the final bunsetsus of clauseboundary units. Next, we discuss the problem ofour method’s inability to parse dependency relations over clause boundaries.
5.1 Parsing Accuracy for Bunsetsu within a
Clause Boundary Unit (except final
bunsetsu)
Table 6 compares parsing accuracies for bunsetsus
within clause boundary units (except the final bunsetsus) between the conventional method and our
method. There are 3,061 bunsetsus within clauseboundary units except the final bunsetsu, among
which 2,499 were correctly parsed by both methods. There were 202 dependency relations correctly parsed by our method but incorrectly parsed
by the conventional method. This means that our
method can narrow down the candidates for a headbunsetsu.
In contrast, 93 dependency relations were correctly parsed solely by the conventional method.
Among these, 46 were dependency relations overclause boundaries, which cannot in principle beparsed by our method. This means that our method
can correctly parse almost all of the dependency
relations that the conventional method can correctly parse except for dependency relations overclause boundaries.
5.2 Parsing Accuracy for Final Bunsetsu of a
Clause Boundary Unit
We can see from Table 4 that the parsing accuracy
for the final bunsetsus of clause boundary units byboth methods is much worse than that for bunsetsus within the clause boundary units (except the
final bunsetsus). This means that it is difficult

Table 7: Comparison of parsing accuracy between
conventional method and our method (for final
bunsetsu of a clause boundary unit)```

our method```
correct
conv. method ````
correct
1037
103
incorrect
total
1,140

incorrect

total

63
534
597

1,100
637
1,737

Table 8: Parsing accuracy for dependency relations over clause boundaries
recallprecision

our method
1.3% (2/152)
11.8% (2/ 17)

conv. method
30.3% (46/152)
25.3% (46/182)

to identify dependency relations whose dependentbunsetsu is the final one of a clause boundary unit.
Table 7 compares how the two methods parsethe dependency relations when the dependent bunsetsu is the final bunsetsu of a clause boundary unit. There are 1,737 dependency relations
whose dependent bunsetsu is the final bunsetsu ofa clause boundary unit, among which 1,037 werecorrectly parsed by both methods. The numberof dependency relations correctly parsed only by
our method was 103. This number is higher thanthat of dependency relations correctly parsed byonly the conventional method. This result might
be attributed to our method’s effect; that is, ourmethod narrows down the candidates internally fora head bunsetsu based on the first-parsed dependency structure for clause boundary units.
5.3 Dependency Relations over Clause
Boundaries
Table 8 shows the accuracy of both methods forparsing dependency relations over clause boundaries. Since our method parses based on the assumption that those dependency relations do notexist, it cannot correctly parse anything. Although, from the experimental results, our method
could identify two dependency relations overclause boundaries, these were identified only because dependency parsing for some sentences was
performed based on wrong clause boundaries thatwere provided by clause boundary analysis. On
the other hand, the conventional method correctly
parsed 46 dependency relations among 152 thatcrossed a clause boundary in the test data. Since
the conventional method could correctly parse
only 30.3% of those dependency relations, we cansee that it is in principle difficult to identify thedependency relations.

6 Related Works
Since monologue sentences tend to be long andhave complex structures, it is important to consider the features. Although there have beenvery few studies on parsing monologue sentences,some studies on parsing written language havedealt with long-sentence parsing. To resolve thesyntactic ambiguity of a long sentence, some ofthem have focused attention on the “clause.”
First, there are the studies that focused attention on compound clauses (Agarwal and Boggess,1992; Kurohashi and Nagao, 1994). These tried
to improve the parsing accuracy of long sentences
by identifying the boundaries of coordinate structures. Next, other research efforts utilized the three
categories into which various types of subordinate
clauses are hierarchically classified based on the“scope-embedding preference” of Japanese subordinate clauses (Shirai et al., 1995; Utsuro et al.,2000). Furthermore, Kim et al. (Kim and Lee,2004) divided a sentence into “S(ubject)-clauses,”
which were defined as a group of words containing
several predicates and their common subject. The
above studies have attempted to reduce the parsing ambiguity between specific types of clauses inorder to improve the parsing accuracy of an entire
sentence.
On the other hand, our method utilizes all typesof clauses without limiting them to specific typesof clauses. To improve the accuracy of longsentence parsing, we thought that it would be more
effective to cyclopaedically divide a sentence into
all types of clauses and then parse the local dependency structure of each clause. Moreover,since our method can perform dependency parsing clause-by-clause, we can reasonably expect
our method to be applicable to incremental parsing (Ohno et al., 2005a).

7 Conclusions
In this paper, we proposed a technique for dependency parsing of monologue sentences basedon clause-boundary detection. The method canachieve more effective, high-performance spokenmonologue parsing by dividing a sentence into
clauses, which are considered as suitable language
units for simplicity. To evaluate the effectivenessof our method for Japanese spoken monologue, we
conducted an experiment on dependency parsingof the spoken monologue sentences recorded in
the “Asu-Wo-Yomu.” From the experimental re-sults, we confirmed that our method shortened theparsing time and increased the parsing accuracy
compared with the conventional method, whichparses a sentence without dividing it into clauses.
Future research will include making a thoroughinvestigation into the relation between dependency
type and the type of clause boundary unit. After
that, we plan to investigate techniques for identifying the dependency relations over clause boundaries. Furthermore, as the experiment described inthis paper has shown the effectiveness of our technique for dependency parsing of long sentencesin spoken monologues, so our technique can be
expected to be effective in written language also.
Therefore, we want to examine the effectivenessby conducting the parsing experiment of long sentences in written language such as newspaper articles.

8

Acknowledgements

This research was supported in part by a contract
with the Strategic Information and Communications R&D Promotion Programme, Ministry of Internal Affairs and Communications and the Grandin-Aid for Young Scientists of JSPS. The first author is partially supported by JSPS Research Fellowships for Young Scientists.

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