Paraphrasing of Japanese Light-verb Constructions
Based on Lexical Conceptual Structure
Atsushi Fujita† Kentaro Furihata† Kentaro Inui† Yuji Matsumoto† Koichi Takeuchi‡†
Graduate School of Information Science,
Nara Institute of Science and Technology{atsush-f,kenta-f,inui,matsu}@is.naist.jp‡
Department of Information Technology,
Okayama University
koichi@it.okayama-u.ac.jp
Abstract
Some particular classes of lexical paraphrases suchas verb alteration and compound noun decomposition can be handled by a handful of general rules
and lexical semantic knowledge. In this paper, we
attempt to capture the regularity underlying theseclasses of paraphrases, focusing on the paraphrasing of Japanese light-verb constructions (LVCs).
We propose a paraphrasing model for LVCs thatis based on transforming the Lexical Conceptual
Structures (LCSs) of verbal elements. We also propose a refinement of an existing LCS dictionary. Experimental results show that our LCS-based paraphrasing model characterizes some of the semantic
features of those verbs required for generating paraphrases, such as the direction of an action and therelationship between arguments and surface cases.

1 Introduction
Automatic paraphrase generation technology offers
the potential to bridge gaps between the authors andreaders of documents. For example, a system thatis capable of simplifying a given text, or showing
the user several alternative expressions conveying
the same content, would be useful for assisting areader (Carroll et al., 1999; Inui et al., 2003).
In Japanese, like other languages, there are several classes of paraphrasing that exhibit a degreeof regularity that allows them to be explained bya handful of sophisticated general rules and lexical
semantic knowledge. For example, paraphrases associated with voice alteration, verb/case alteration,
compounds, and lexical derivations all fall into suchclasses. In this paper, we focus our discussion on
another useful class of paraphrases, namely, theparaphrasing of light-verb constructions (LVCs),
and propose a computational model for generating
paraphrases of this class.
Sentence (1s) is an example of an LVC1 . An LVC
is a verb phrase (“kandou-o ataeta (made an impression)” in (1s)) that consists of a light-verb (“ataeta(give-PAST)”) that grammatically governs a nomi1

For each example, s denotes an input and t denotes its paraphrase.

nalized verb (“kandou (an impression)”) (also see
Figure 1 in Section 2.2). A paraphrase of (1s) is sentence (1t), in which the nominalized verb functions
as the main verb with its verbal form (“kandou-s-as
e-ta (be impressed-C AU , PAST)”).
(1) s. Eiga-ga kare-ni kandou-o
ataeta.
film- NOM him-DAT impression-ACC give-PASTThe film made an impression on him.

t. Eiga-ga kare-o kandou-s-ase-ta.
film- NOM him-ACC be impressed-CAUSATIVE , PASTThe film impressed him.

To generate this type of paraphrase, we need a computational model that is capable of the following
two classes of choice (also see Section 2.2):
Selection of the voice: The model needs to be able
to choose the voice of the target sentence from
active, passive, causative, etc. In example (1),the causative voice is chosen, which is indicated by the auxiliary verb “ase (causative)”.
Reassignment of the cases: The model needs tobe able to reassign a case marker to each argument of the main verb. In (1), the grammatical case of “kare (him),” which was originally assigned the dative case, is changed toaccusative.
The task is not as simple as it may seem, because
both decisions depend not only on the syntactic andsemantic attributes of the light-verb, but also on
those of the nominalized verb (Muraki, 1991).
In this paper, we propose a novel lexical
semantics-based account of the LVC paraphrasing,which uses the theory of Lexical Conceptual Structure (LCS) of Japanese verbs (Kageyama, 1996;
Takeuchi et al., 2001). The theory of LCS offers
an advantage as the basis of lexical resources forparaphrasing, because it has been developed to explain varieties of linguistic phenomena including
lexical derivations, the construction of compounds,
and verb alteration (Levin, 1993; Dorr et al., 1995;
Kageyama, 1996; Takeuchi et al., 2001), all ofwhich are associated with the systematic paraphrasing we mentioned above.
The paraphrasing associated with LVCs is not idiosyncratic to Japanese but also appears commonly

(a) Adverb

in other languages such as English (Mel’čuk andPolguère, 1987; Iordanskaja et al., 1991; Dras,
1999, etc.), as indicated by the following examples.
(2) s. Steven made an attempt to stop playing.
t. Steven attempted to stop playing.
(3) s. It had a noticeable effect on the trade.
t. It noticeably affected the trade.
Our approach raises the interesting issue of whether
the paraphrasing of LVCs can be modeled in an
analogous way across languages.
Our aim in this paper are: (i) exploring the regularity of the LVC paraphrasing based a lexical
semantics-based account, and (ii) assessing the immature Japanese semantic typology through a practical task.
The following sections describe our motivation, target, and related work on LVC paraphrasing (Section 2), the basics of LCS and the refinements we made (Section 3), our paraphrasing model(Section 4), and our experiments (Section 5). Finally, we conclude this paper with a brief of description of work to be done in the future (Section 6).

2 Motivation, target, and related work
2.1 Motivation
One of the critical issues that we face in paraphrase generation is how to develop and maintainknowledge resources that covers a sufficiently wide
range of paraphrasing patterns such as those indicating that “to make an attempt” can be paraphrased into “to attempt,” and that “potential” canbe paraphrased into “possibility.” Several attempts
have been made to develop such resources manually(Sato, 1999; Dras, 1999; Inui and Nogami, 2001);
those work have, however, tended to restrict theirscope to specific classes of paraphrases, and cannot
be used to construct a sufficiently comprehensive resource for practical applications.
There is another trend in the research in this field,
namely, the automatic acquisition of paraphrase patterns from parallel or comparable corpora (Barzilay
and McKeown, 2001; Lin and Pantel, 2001; Pang etal., 2003; Shinyama and Sekine, 2003, etc.). This
type of approach may be able to reduce the costof resource development. There are problems thatmust be overcome, however, before they can work
practically. First, automatically acquired patterns
tend to be complex. For example, from the paraphrase of (4s) into (4t), we can naively obtain thepattern: “X is purchased by Y ⇒ Y buys X.”(4) s. This car was purchased by him.
t. He bought this car.
This could also, however, be regarded as a combination of a simpler pattern of lexical paraphrasing(“purchase ⇒ buy”) and a voice activization (“X(b)

Noun + Case Particle(c) Noun + Case particle"no"(GEN)(d) Embedded clause(e) Adjective
Target of this paper Nominalized verb + Case particle
LVC Light-verb (+suffixes)

Figure 1: Dependency structure showing the range
which the LVC paraphrasing affects.
be VERB- PP by Y ⇒ Y VERB X”). If we were touse an acquisition scheme that is not capable of decomposing such complex paraphrases correctly, we
would have to collect a combinatorial number ofparaphrases to gain the required coverage. Second,
the results of automatic acquisition would likely include many inappropriate patterns, which would require manual correction. Manual correction, however, would be impractical if we were collecting a
combinatorial number of patterns.
Our approach to this dilemma is as follows: first,
we manually develop the resources needed to coverthose paraphrases that appear regularly, and then decompose and automatically refine the acquired paraphrasing patterns using those resources. The work
reported in this paper is aimed at this resource development.
2.2 Target structure and required operations
Figure 1 shows the range which the LVC paraphrasing affects, where the solid boxes denote
Japanese base-chunk so-called “bunsetsu.”2 Being
involved in the paraphrasing, the modifiers of the
LVC need the following operations:
Change of the dependence: The dependences ofthe elements (a) and (b) need to be changed
because the original modifiee, the light-verb,
is eliminated by the paraphrasing.
Re-conjugation: The conjugation form of the elements (d), (e), and occasionally (c) need to bechanged according to the category change of
their modifiee, the nominalized verb.
Reassignment of the cases: As described in theprevious section, the case markers of the elements (b) and often (c) need to be reassigned.
Selection of the voice: The voice of the nominalized verb needs to be chosen according to the
combination of the nominalized verb, the lightverb, and the original voice.
The first two operations are trivial in the field oftext generation. Moreover, they can be done independently of the LVC paraphrasing. The most delicate operation is for the element (c) because it acts
either as an adverb or as a case, relying on the con2

The modifiee of the LVC is not affected because the partof-speech of the light-verb and main verb are the same.

Table 1: Examples of LCS
Verb
move
transmit
locatemaintain

LCS for verb[y MOVE TO z][x CONTROL [y MOVE TO z]][y BE AT z][x CONTROL [y BE AT z]]

Verb phrase
My sister (Theme) moves to a neighboring town (Goal).
The enzyme (Agent) transmits messages (Theme) to the muscles (Goal).
The school (Theme) locates near the river (Goal).
He (Agent) maintains a machine (Theme) in good condition (Goal).

text. In the former case, it needs the second operation. In the latter case, it needs the third operation
as well as the element (b).
In this paper, we take into account only the element (b), namely, the sibling cases of the nominalized verb.
2.3 Related work
Based on the Meaning-Text Theory (Mel’čuk andPolguère, 1987), Iordanskaja et al. (1991) proposes a set of paraphrasing rules including one for
LVC paraphrasing. Their rule heavily relies on what
are called lexical functions, by which they virtuallyspecify all the choices relevant to LVC paraphrasing for every combination of nominalized verb andlight-verb individually. Our approach is to employ
lexical semantics to provide a general account of
those classes of choices.
On the other hand, Kaji and Kurohashi (2004)
proposes a paraphrasing model which bases on an
ordinary dictionary. Given an input LVC, theirmodel paraphrases it using the gloss of both thenominalized verb and the light-verb with the semantic feature of the light-verb. Their model looks robust because of the availability of an ordinary dictionary. However, their model fails to explain the
difference in the voice selection between examples(5) and (6) since it selects the voice based only
on the light-verb — in their approach, the lightverb “ukeru (to receive)” always maps to the passive
voice irrespective of the nominalized verb.
(5) s. Enkai-eno shoutai-ouketa.
party-GEN invitation-ACC receive-PASTI received an invitation to the party.

t. Enkai-ni shoutai-s-are-ta.
party-DAT invite-PAS , PASTI was invited to the party.

(6) s. Kare-no
his-GEN

hanashi-ni
talk-DAT

kandou-ouketa.

impression-ACC receive-PASTI was given a good impression by his talk.

t. Kare-no hanashi-ni kandou-shi-ta.
his-GEN talk-DAT
be impressed-ACT , PASTI was impressed by his talk.

In (Kaji and Kurohashi, 2004), the target expression is restricted only to the LVC itself (also see
Figure 1). Hence, their model is unable to reassign
the cases as we saw in example (1).

3 Lexical Conceptual Structure
3.1 Basic framework of LCS
The theory of Lexical Conceptual Structure (LCS)
associates a verb with a semantic structure as exemplified by Table 1. An LCS consists of semantic predicates (“CONTROL,” “BE AT,” etc.)
and their argument slots (x, y, z). Argument
slots x, y, and z correspond to the semantic roles“Agent,” “Theme,” and “Goal,” respectively. Taking the LCS of the verb “transmit” as an example,[y MOVE TO z] denotes the state of affairs that thestate of the “Theme” changes to the “Goal,” and[x CONTROL . . .] denotes that the “Agent” causes thestate change.
3.2 Refinements
We make use of the TLCS dictionary, a Japaneseverb LCS dictionary developed by Takeuchi et al.
(2001), because it offers the following advantages:• It is based on solid linguistic work, as in(Kageyama, 1996).
• Its scale is considerably larger than any otherexisting collections of verb LCS entries.
• It provides a set of concrete rules for LCS assignment, which ensures the reliability of thedictionary.
In spite of these advantages, our preliminary examination of the dictionary revealed that further refinements were needed. To refine the typology of
TLCS, we collected the following sets of words:
Nominalized verbs: We regard “sahen-nouns”3and nominal forms of verbs as nominalized
verbs. We retrieved 1,210 nominalized verbs
from the TLCS dictionary.
Light-verbs: Since a verb takes different meanings
when it is a part of LVCs with different caseparticles, we collected pairs c, v of case particle c and verb v in the following way:
Step 1. We collected 876,101 types of triplets
n, c, v of nominalized verb n, case particle c, and base form of verb v from theparsed4 sentences of newspaper articles5 .
3A sahen-noun is a verbal noun in Japanese, which acts asa verb in the form of “sahen-noun + suru”.
4
We used the statistical Japanese dependency parser
CaboCha (Kudo and Matsumoto, 2002) for parsing.
http://chasen.naist.jp/˜taku/software/cabocha/
5
Excerpts from 9 years of the Mainichi Shinbun and 10
years of the Nihon Keizai Shinbun, giving a total of 25,061,504
sentences, were used.

Table 2: Extensions of LCS
Ext.1

Verb
hankou-suru(resist)

Ext.2

ukeru
(receive)Ext.3

motomeru
(ask)

Ext.4

kandou-suru(be impressed)

Verb phrase and its LCS representation[[Ken]y BE AGAINST [parents]z]
Ken-gaoya-nihankou-suru.
Ken-NOM parents-DAT resist- PRES
(Ken resists his parents.)[BECOME [[salesclerk]z BE WITH [[complaint]y MOVE FROM [customer]x TO [salesclerk]z]]]Ten’in-ga
kyaku-karakujo-oukeru.
salesclerk-NOM customer-ABL complaint-ACC receive-PRES(The salesclerk receives a complaint from a customer.)[[Ken]x CONTROL [[apology]y MOVE FROM [George]z TO [FILLED]]]6
Ken-gaGeorge-ni shazai-o
motomeru.
Ken-NOM George-DAT apology-ACC ask-PRES(Ken asks George for an apology.)[BECOME [[Ken]z BE WITH [[FILLED]y MOVE FROM [music]x TO [Ken]z]]]
Ken-gaongaku-ni kandou-suru.
Ken-NOM music-DAT be impressed-PRES(Ken is impressed by the music.)

Step 2. For each of the 50 most frequent c, v
tuples, we extracted the 10 most frequentn, c, v.
Step 3. Each n, c, v was manually evaluatedto determine whether it was an LVC. If
any of 10 triplets was determined to be
an LVC, c, v was merged into the list oflight-verbs. As a result, we collected 40
types of c, v for light-verbs.
Through investigating the above 1,210 nominalized verbs and 40 light-verbs, we extended the typology of TLCS as shown below (also see Table 2).
Ext. 1. Treatment of “Partner”: The dative caseof “hankou-suru (resist)” and “eikyo-suru (affect)” does not indicate the “Goal” of the action but the “Partner.”
Ext. 2. Verbs of obtaining (Levin, 1993): In contrast with “ataeru (give),” the nominative caseof “ukeru (receive)” and “eru (acquire)” is the“Goal” of the “Theme,” while the ablative caseindicates “Source.”
Ext. 3. Require verb: “motomeru (ask)” and“yokyu-suru (require)” denote the existence ofthe external “Agent” who controls the action of
the other “Agent” or “Theme.”
Ext. 4. Verbs of psychological state (Levin,1993): “kandou-suru (be impressed)” and “osoreru (fear)” indicate the change of psychological state of the “Agent.” The ascriptive part ofthe change has to be described.
Consequently, we defined a new LCS typology
consisting of 16 types. Note that more than one LCS
can be assigned to a verb if it has a polysemy. For
convenience, we refer to the extended dictionary as
the LCSdic7 .
6
The predicate “FILLED” represents an implicit argumentof the verb and the verb assigned this LCS cannot take this argument. In the LCS of the verb “sign”, for example, “FILLED”
in [x CONTROL [BECOME [[FILLED]y BE AT z]]] denotes
the name of “Agent.”
7
The latest version of the LCSdic is available fromhttp://cl.it.okayama-u.ac.jp/rsc/lcs/

4 Paraphrasing model
In this section, we describe how we generate paraphrases of LVCs. Figure 2 illustrates how our model
paraphrases the LVC of example (7).
(7) s. Ken-ga eiga-ni shigeki-ouketa.
Ken-NOM film- DAT inspiration-ACC receive-PAST
Ken received an inspiration from the film.

t. Ken-ga eiga-ni shigeki-s-are-ta.
Ken-NOM film- DAT inspire-PAS , PAST
Ken was inspired by the film.

The idea is to exploit the LCS representation as asemantic representation and to model the LVC paraphrasing by the transformation of the LCS representation. The process consists of the following threesteps:
Step 1. Semantic analysis: The model first analyzes a given input sentence including an LVC
to obtain its semantic structure in terms of the
LCS representation. In Figure 2, this step produces LCSV 1 .
Step 2. Semantic transformation (LCS transformation): The model then transfers the obtained semantic structure to another semantic
structure so that the target structure consists of
the LCS of the nominalized verb of the input.
In our example, this step generates LCSN 1 together with the supplement [BECOME [. . .]].
Step 3. Surface generation: Having obtained thetarget LCS representation, the model finally
lexicalizes it to generate the output sentence.
So, the key issue is how to control the second step,
namely, the transformation of the LCS representation.
The rest of this section elaborates on each step,using different symbols to denote arguments; x, y,
and z for LCSV , and x’, y’, and z’ for LCSN .
4.1 Semantic analysis
Given an input sentence, which we assume to be asimple clause with an LVC, we first look up the LCS
template LCSV 0 for the given light-verb, and thenapply the case assignment rule, below (Takeuchi etLCS dictionary

Input sentence
Ken-ga (Ken-NOM) eiga-ni (film-DAT)
shigeki-o (inspiration-ACC) uketa (receive-PAST).

[BECOME [z BE WITH [y MOVE FROM x TO z]]]ukeru (receive) LCSV0(1) Semantic analysis
LCSV1 [BECOME [[Ken]z BE WITH[[inspiration]y MOVE FROM [film]x TO [Ken]z]]]shigeki-suru (inspire) LCSN0[x’ ACT ON y’]

LCSV1 [BECOME [[Ken]z BE WITH[[inspiration]y MOVE FROM [film]x TO [Ken]z]]](a) Predicate matching(b) Argument matching

LCSN0 [x’ ACT ON y’](c) Attaching the remaining structure

LCSN1[BECOME [[Ken]z BE WITH]] + [[film]x’ ACT ON [Ken]y’]Figure 3: LCS transformation.
(2) LCS transformation[BECOME [[Ken]z BE WITH]] + [[film]x’ ACT ON [Ken]y’]
LCSN1(3) Surface generation
Paraphrased sentence
Ken-ga (Ken-NOM) eiga-ni (film-DAT)
shigeki-s-are-ta (inspire-PAS, PAST).

Figure 2: The LCS-based paraphrasing model.
al., 2001), to obtain its LCS representation LCSV 1 :Case assignment rule:• In the case of the LCSV 0 having argument x,
fill the leftmost argument of the LCSV 0 with
the nominative case of the input, the second
leftmost with the accusative, and the rest withthe dative.
• Otherwise, fill arguments y and z of the LCSV 0
with the nominative and the dative, respectively.
In the example shown in Figure 2, the nominative“Ken” fills the leftmost argument z. Accordingly,
the accusative “shigeki (inspiration)” and the dative“eiga (film)” fill y and x, respectively.
(8) s. Ken-ga eiga-ni shigeki-ouketa.
Ken-NOM film- DAT inspiration-ACC receive-PAST
Ken received an inspiration from the film.
LCSV 0 [BECOME [z BE WITH [y MOVE FROM x
TO z]]]LCSV 1 [BECOME [[Ken]z BE WITH [[inspiration]y
MOVE FROM [film]x TO [Ken]z]]]

4.2 LCS transformation
The second step of our paraphrasing model
matches the resultant LCS representation (LCSV 1
in Figure 2) with the LCS of the nominalized verb
(LCSN 0 ) to generate the target LCS representation(LCSN 1 ). Figure 3 shows a more detailed view ofthis process for the example shown in Figure 2.
4.2.1 Predicate matching
The first step is to determine the predicate in
LCSV 1 that should be matched with the predicate
in LCSN 0 . Assuming that only the agentivity is relevant to the selection of the voice in the paraphrasing of LVC, which is our primary concern, we classify the semantic predicates into the following twoclasses:
Agentive predicates: “CONTROL,” “ACT ON,”
“ACT,” “BE AGAINST,” and “MOVE FROMTO.”State of affair predicates: “MOVE TO,” “BEAT,” and “BE WITH.”
Aspectual predicates: “BECOME.”We also assume that any pair of predicates ofthe same class is allowed to match, and that the
aspectual predicates are ignored. In our example,“MOVE FROM TO” matches “ACT ON,” as shownin Figure 3.
LCS representations have right-branching (orright-embedding) structures. Since inner-embedded
predicates denote the state of affairs, they take priority in the matching. In other words, the matchingproceeds from the rightmost inner predicates to theouter predicates.
Having matched the predicates, we then fill eachargument slot in LCSN 0 with its corresponding argument in LCSV 1 . In Figure 3, argument z ismatched with y’, and x with x’. As a result, “Ken”
comes to the y’ slot and “eiga (film)” comes to thex’ slot8 .
This process is repeated until the leftmost predicate in LCSN 0 or that in LCSV 1 is matched.
4.2.2 Treatment of non-transfered predicates
If LCSV 1 has any non-transfered predicates when
the predicate matching has been completed, they
represent the semantic content that is not covered by
LCSN 1 and which needs to be lexicalized by auxiliary linguistic devices such as voice auxiliaries.
In the case of Figure 3, [BECOME [[Ken]z BE WITH]]
in LCSV 1 remains non-transfered. In such a case,
we attach the non-transfered predicates to LCSN 0 ,which are then lexicalized by auxiliaries in the next
step, the surface generation.
4.3 Surface generation
We again apply the aforementioned case assignment
rule to generate a sentence from the resultant LCS
representation. In this process, the model makes the
final decisions on the selection of the voice and thereassignment of the cases, according to the following decision list:
8
When an argument is filled with another LCS, arguments
within the inner LCS are also matched. Likewise, with regard
to an assumption that the input sentences are periphrastic, we
introduced some exceptional rules. That is, arguments filledwith the implicit filler represented by “FILLED” or the target
nominalized verb N are never matched, and “Goal” in LCSV 1
can be matched to “Theme” in LCSN 0 .

1. If the attached predicate is filled with the same
argument as the leftmost argument in LCSN 1 ,the “active” voice is selected and the casestructure is left as is.
2. If the argument of the attached predicate has
the same value as either z’ or y’ in LCSN 1 ,lexicalization is performed to make the argument a subject. Therefore, the “passive” voice
is selected and case alternation (passivization)
is applied.
3. If the attached predicate is “BE WITH” and its
argument has the same value as x’ in LCSN 1 ,the “causative” voice is selected and case alternation (causativizaton) is applied.
4. If the attached predicate is an agentive predicate, and its argument is filled with a value different from those of the other arguments, thenthe “causative” voice is selected and case alternation (causativization) is applied.
5. Otherwise, no modification is applied.
Since the example in Figure 2 satisfies the second
condition, the model chooses “s-are-ru (passive)”and passivizes the sentence so that “Ken” fills thenominative case.
(9) LCSN 1 [BECOME [[Ken]z BE WITH]]+ [[film]x’ ACT ON [Ken]y’]t. Ken-ga eiga-ni shigeki-s-are-ta.
Ken-NOM film- DAT inspire-PAS , PAST
Ken was inspired by the film.

5 Experiment
5.1 Paraphrase generation and evaluation
To empirically evaluate our paraphrasing model andthe LCSdic, and to clarify the remaining problems,
we analyzed a set of automatically generated paraphrase candidates. The sentences used in the experiment were collected in the following way:
Step 1. From the 876,101 types of triplet n, c, v
collected in Section 3.2, 23,608 types of
n, c, v were extracted, whose components, nand c, v, are listed in the LCSdic.
Step 2. For each of the 245 most frequent n, c, v,the 3 most frequent simple clauses including the n, c, v were extracted from the corpus from which n, c, v s were extracted in
Section 3.2. As a result, we collected 735 sentences.
Step 3. We input these 735 sentences into our paraphrasing model, and then automatically generated paraphrase candidates. When more than
one LCS is assigned to a verb in the LCSdic
due to its polysemy or ergative verb such as“kaifuku-suru (recover),” our model generates
all the possible paraphrase candidates. As a result, 825 paraphrase candidates, that is, at least
one for each input, were generated.

Table 3: Error sources
Correct candidates
Erroneous candidates
Definition of LCS
LCS for light-verb
LCS for nominalized verb
Paraphrasing modelLCS transformation algorithm
Treatment of “suru (to do)”Ambiguity
Ambiguous thematic role of dative
Recognition of LVC
Selection of transitive/intransitive

621 (75.8%)
198 (24.2%)
30
24
6
61
59
2
107
78
24
5

We manually classified the resultant 825 paraphrase candidates into 621 correct and 198 erroneous candidates. The remaining 6 candidates were
not classified. The precision of the paraphrase generation was 75.8% (621 / 819).
5.2 Error analysis
To clarify the cause of the erroneous paraphrases,
we manually classified 198 erroneous paraphrase
candidates. Table 3 lists the error sources.
5.2.1 LCS transformation algorithm
The experiment came close to confirming that theright-first matching algorithm in our paraphrasing
model operates correctly. Unfortunately, the matching rules produced some erroneous paraphrases in
LCS transformation.
Errors in predicate matching: To paraphrase(10s) below, “CONTROL” in LCSV 1 must be
matched with “CONTROL” in LCSN 0 , and x to x’.
However, our model first matched “CONTROL” in
LCSV 1 with “MOVE FROM TO” in LCSN 0 . Thus,
x was incorrectly matched with z’ and x’ remained
empty. The desired form of LCSN 1 is shown in(11).
(10) s. kacho-ga
buka-nisection-chief-NOM

shiji-osubordinate-DAT

dasu.

order-ACC issue-PRES
The section chief issues orders to his subordinates.

(N =“order”, V =“issue”)

LCSV 1 [[chief ]x CONTROL [BECOME [[order]yBE AT [subordinate]z]]]LCSN 0 [x’ CONTROL [y’ MOVE FROM z’ TO[FILLED]]]LCSN 1 ∗ [x’ CONTROL [[subordinate]y’ MOVEFROM [chief ]z’ TO [FILLED]]](11) LCSN 1 [[chief ]x’ CONTROL [y’ MOVE FROM
[subordinate] TO [FILLED]]]

This error was caused by the mis-matching of“CONTROL” with “MOVE FROM TO.” Although
we regard some predicates as being in the sameclasses as those described in Section 4.2.1, theseneed to be considered carefully. In particular“MOVE FROM TO” needs further investigation because it causes many errors whenever it has the“FILLED” argument.
Errors in argument matching: Even if all thepredicates are matched properly, there would still
be a chance of errors being caused by incorrect argument matching. With the present algorithm, z
can be matched with y’ if and only if z’ contains“FILLED.” In the case of (12), however, z has tobe matched with y’, even though z’ is empty. The
desired form of LCSN 1 is shown in (13).
(12) s. Jikan-ni seigen-ga
aru.
time- DAT limitation- NOM exist-PRES
There is a time limitation.

(N =“limitation”, V =“exist”)

LCSV 1 [[limitation]y BE AT [time]z]
LCSN 0 [x’ CONTROL [BECOME [y’ BE AT z’]]]LCSN 1 ∗ [x’ CONTROL [BECOME [y’ BE AT[time]z’]]](13) LCSN 1 [x’ CONTROL [BECOME [[timey]y’ BE AT
z’]]]

5.2.2 Ambiguous thematic role of dative
In contrast to dative cases in English, in Japanese,the dative case has ambiguity. That is, it can be acomplement to the verb or an adjunct9 . However,since LCS is not capable of determining whether thecase is a complement or an adjunct, z is occasionally incorrectly filled with an adjunct. For example, “medo-ni” in (14s) should not fill z, because it
acts as an adverb, even though it consists of a noun,“medo (prospect)” and a case particle for the dative.
We found that 78 erroneous candidates constitutethis most dominant type of errors.
(14) s. Kin’you-o medo-ni sagyo-o susumeru.
Friday-NOM by-DAT
work-ACC carry on-PRES
I plan to finish the work by Friday.

(N =“work”, V =“carry”)

LCSV 0 [x CONTROL [BECOME [y BE AT z]]]LCSV 1 ∗ [x CONTROL [BECOME [[work]y BE AT[by]z]]]

The ambiguity of dative cases in Japanese has
been discussed in the literature of linguistics andsome natural language processing tasks (Muraki,1991). To date, however, a practical compliment/adjunct classifier has not been established. We
plan to address this topic in our future research.
Preliminary investigation revealed that only certain groups of nouns can constitute both compliments and adjuncts according to the governing verb.
Therefore, generally whether a word acts as a complement is determined without combining it with theverb.
9(Muraki, 1991) classifies dative cases into 11 thematicroles that can be regarded as complements. In contrast, there
is no typology of dative cases that act as adjuncts.

5.2.3 Recognition of LVC
In our model, we assume that a triplet n, c, v consisting of a nominalized verb n and a light-verb tuple c, v from our vocabulary lists (see Section 3.2)
always act as an LVC. However, not only the triplet
itself but also its context sometimes affects whether
the given triplet can be paraphrased. For example, we regard “imi-ga aru” as an LVC, because thenominalized verb “imi” and the tuple “ga”, “aru”
appear in the vocabulary lists. However, the n, c, v
in (15s) does not act as an LVC, while the same
triplet in (16s) does.
(15) s. Sanka-suru-koto-ni imi-ga
aru.
to participate-DAT
meaning-NOM exist-PRES
There is meaning in participating.

t.∗ Sanka-suru-koto-o imi-suru.
∗

to participate-ACC
mean-ACT , PRES
It means to participate in it.

(16) s. “kennel”-niwa“kennel”-TOP

imi-gainugoya-toiu
doghouse-OFaru.

meaning-NOM exist-PRES“kennel” has the meaning of doghouse.

t. “kennel”-wa inugoya-oimi-suru.

“kennel”-TOP doghouse-ACC mean-ACT , PRES
“kennel” means doghouse.

The above difference is caused by the polysemyof the nominalized verb “imi” that denotes “worth”
in the context of (15s), but “meaning” in (16s).
Although incorporating word sense disambiguation
using contextual clues complicates our model, infact only a limited number of nominalized verbs arepolysemous. We therefore expect that we can list
them up and use this as a trigger for making a decision as to whether we need to take the context into
account. Namely, given a n, c, v, we would be
able to classify it into (a) a main verb phrase, (b) adelicate case in terms of the dependence of its context, and (c) an LVC.
We can adopt a different approach to avoiding
incorrect paraphrase generation. As described in
Section 5.1, our model generates all the possible
paraphrase candidates when more than one LCS is
assigned to a verb. Similarly, our approach can beextended to (i) over-generate paraphrase candidates
by considering the polysemy of not only assigned
LCS types, but also that of nominalized verbs (see(15s) and (16s)) and whether the given n, c, v isan LVC, and (ii) revise or reject the incorrect candidates by using handcrafted solid rules or statisticallanguage models.

6 Conclusion and future work
In this paper, we presented an LCS-based paraphrasing model for LVCs and an extension of an ex-isting LCS dictionary. Our model achieved an accuracy of 75.8% in selecting the voice and reassigning
the cases.
To make our paraphrasing model more accurate,further analysis is needed, especially for the LCS
transformation stage described in Section 4.2. Similarly, several levels of disambiguation should also
be solved. The Japanese LCS typology has to berefined from the theoretical point of view. For example, since extensions are no more than human intuition, we must discuss how we can assign LCSs
for given verbs based on explicit language tests, as
described in (Takeuchi et al., 2001).
In future research, we will also extend our LCSbased approach to other classes of paraphrases thatexhibit some regularity, such as verb alteration andcompound noun decomposition as shown in (17)and (18), below. LCS has been discussed as ameans of explaining the difference between transitive/intransitive verbs, and the construction of compounds. Therefore, our next goal is to show the applicability of LCS through practical tasks, namely,
paraphrasing.
(17) s. Jishin-ga
building-o kowashita.
earthquake-NOM building-DAT destroy-PASTThe earthquake destroyed the building.

t. Jishin-debuilding-ga kowareta.

earthquake-LOC building-NOM be destroyed-PASTThe building was destroyed in the earthquake.

(18) s. Kare-wa
he-TOPkikaimachine-sousa-gajouzu-da.

operation-NOM good-COPULA
He is good at operating the machine.

t. Kare-wa kikai-ojouzu-ni sousa-suru.

he-TOPmachine-DAT well- ADV operate-PRES
He operates machines well.

References
R. Barzilay and K. R. McKeown. 2001. Extracting paraphrases from a parallel corpus. In Proceedings of the39th Annual Meeting of the Association for Computational Linguistics (ACL), pages 50–57.
J. Carroll, G. Minnen, D. Pearce, Y. Canning, S. Devlin, and J. Tait. 1999. Simplifying text for languageimpaired readers. In Proceedings of the 9th Conference of the European Chapter of the Association for
Computational Linguistics (EACL), pages 269–270.
B. J. Dorr, J. Garman, and A. Weinberg. 1995. Fromsyntactic encodings to thematic roles: building lexical entries for interlingual MT. Machine Translation,
9(3):71–100.
M. Dras. 1999. Tree adjoining grammar and the reluctant paraphrasing of text. Ph.D. thesis, Department ofComputing, Macquarie University.
K. Inui and M. Nogami. 2001. A paraphrase-based
exploration of cohesiveness criteria. In Proceedingsof the 8th European Workshop on Natulal Language
Generation (EWNLG), pages 101–110.
K. Inui, A. Fujita, T. Takahashi, R. Iida, and T. Iwakura.
2003. Text simplification for reading assistance: aproject note. In Proceedings of the 2nd International
Workshop on Paraphrasing: Paraphrase Acquisition
and Applications (IWP), pages 9–16.
L. Iordanskaja, R. Kittredge, and A. Polguère. 1991.
Lexical selection and paraphrase in a meaning-text
generation model. In Paris et al. (Eds.) Natural Language Generation in Artificial Intelligence and Computational Linguistics, pages 293–312. Kluwer Academic Publishers.
T. Kageyama, editor. 1996. Verb semantics. Kuroshio
Publishers. (in Japanese).
N. Kaji and S. Kurohashi. 2004. Recognition and paraphrasing of periphrastic and overlapping verb phrases.
In Proceedings of the 4th International Conference on
Language Resources and Evaluation (LREC) Workshop on Methodologies and Evaluation of Multiword
Units in Real-world Application.
T. Kudo and Y. Matsumoto. 2002. Japanese dependency analysis using cascaded chunking. In Proceedings of 6th Conference on Natural Language Learning(CoNLL), pages 63–69.
B. Levin. 1993. English verb classes and alternations:a preliminary investigation. Chicago Press.
D. Lin and P. Pantel. 2001. Discovery of inference rules
for question answering. Natural Language Engineering, 7(4):343–360.
I. Mel’čuk and A. Polguère. 1987. A formal lexicon inmeaning-text theory (or how to do lexica with words).
Computational Linguistics, 13(3-4):261–275.
S. Muraki. 1991. Various aspects of Japanese verbs.
Hitsuji Syobo. (in Japanese).
B. Pang, K. Knight, and D. Marcu. 2003. Syntax-basedalignment of multiple translations: extracting paraphrases and generating new sentences. In Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL),
pages 102–109.
S. Sato. 1999. Automatic paraphrase of technical papers’ titles. Journal of Information Processing Societyof Japan, 40(7):2937–2945. (in Japanese).
Y. Shinyama and S. Sekine. 2003. Paraphrase acquisition for information extraction. In Proceedings of the
2nd International Workshop on Paraphrasing: Paraphrase Acquisition and Applications (IWP), pages 65–
71.
K. Takeuchi, K. Uchiyama, S. Yoshioka, K. Kageura,and T. Koyama. 2001. Categorising deverbal nouns
based on lexical conceptual structure for analysing
Japanese compounds. In Proceedings of IEEE System, Man, and Cybernetics Conference, pages 904–
909.