A Compositional Approach toward Dynamic Phrasal Thesaurus
Atsushi Fujita Shuhei Kato Naoki Kato Satoshi Sato
Graduate School of Engineering, Nagoya University{fujita,ssato}@nuee.nagoya-u.ac.jp{shuhei,naoki}@sslab.nuee.nagoya-u.ac.jp

Abstract
To enhance the technology for computing
semantic equivalence, we introduce the notion of phrasal thesaurus which is a naturalextension of conventional word-based thesaurus. Among a variety of phrases thatconveys the same meaning, i.e., paraphrases,
we focus on syntactic variants that are compositionally explainable using a small number of atomic knowledge, and develop a system which dynamically generates such variants. This paper describes the proposed system and three sorts of knowledge developed
for dynamic phrasal thesaurus in Japanese:(i) transformation pattern, (ii) generation
function, and (iii) lexical function.

1 Introduction
Linguistic expressions that convey the same meaning are called paraphrases. Handling paraphrases
is one of the key issues in a broad range of natural language processing tasks, including machinetranslation, information retrieval, information extraction, question answering, summarization, text
mining, and natural language generation.
Conventional approaches to computing semantic
equivalence between two expressions are five-fold.
The first approximates it based on the similarities
between their constituent words. If two words belong to closer nodes in a thesaurus or semantic network, they are considered more likely to be similar.
The second uses the family of tree kernels (Collins
and Duffy, 2001; Takahashi, 2005). The degree of
equivalence of two trees (sentences) is defined as
the number of common subtrees included in both
trees. The third estimates the equivalence based onword alignment composed using templates or translation probabilities derived from a set of paralleltext (Barzilay and Lee, 2003; Brockett and Dolan,2005). The fourth espouses the distributional hypothesis (Harris, 1968): given two words are likely
to be equivalent if distributions of their surroundingwords are similar (Lin and Pantel, 2001; Weeds etal., 2005). The final regards two expressions equivalent if they can be associated by using a set of lexicosyntactic paraphrase patterns (Mel’čuk, 1996; Dras,
1999; Yoshikane et al., 1999; Takahashi, 2005).
Despite the results previous work has achieved,
no system that robustly recognizes and generates
paraphrases is established. We are not convinced ofa hypothesis underlying the word-based approaches
because the structure of words also conveys somemeaning. Even tree kernels, which take structures
into account, do not have a mechanism for identifying typical equivalents: e.g., dative alternationand passivization, and abilities to generate paraphrases. Contrary to the theoretical basis, the twolines of corpus-based approaches have problems inpractice, i.e., data sparseness and computation cost.
The pattern-based approaches seem steadiest. Yet
no complete resource or methodology for handlinga wide variety of paraphrases has been developed.
On the basis of this recognition, we introduce thenotion of phrasal thesaurus to directly compute semantic equivalence of phrases such as follows.
(1)a. be in our favor / be favorable for us
b. its reproducibility / if it is reproducible
c. decrease sharply / show a sharp decreased. investigate the cause of a fire /investigate why there was a fire /investigate what started a fire /make an investigation into the cause of a firePhrasal thesaurus is a natural extension of conventional word-based thesaurus. It is thus promised that
it will bring us the following benefits:
Enhancement of NLP applications: As conventional thesauri, phrasal thesaurus must be
useful to handle paraphrases having different
structures in a wide range of NLP applications.
Reading and writing aids: Showing more appropriate alternative phrases must be a powerful aid at certain situations such as writing
text. Controlling readability of text by alteringphrases must also be beneficial to readers.
Our aim is to develop resources and mechanisms
for computing semantic equivalence on the working
hypothesis that phrase is the appropriate unit for that
purpose. This paper describes the first version of ourparaphrase generation system and reports on our ongoing work on constructing resources for realizing
phrasal thesaurus.
The following sections describe the range of phenomena we treat (Section 2), the overall architecture of our paraphrase generation system whichfunctions as phrasal thesaurus (Section 3), the implementation of knowledge bases (Section 4) followed by discussion (Section 5), and conclusion(Section 6).

2 Dynamic phrasal thesaurus
2.1

Issue

Toward realizing phrasal thesaurus, the following
two issues should be discussed.
• What sorts of phrases should be treated• How to cope with a variety of expressions
Although technologies of shallow parsing havebeen dramatically improved in the last decade, it
is still difficult to represent arbitrary expression inlogical form. We therefore think it is reasonable todefine the range relying on lexico-syntactic structure instead of using particular semantic representation. According to the work of (Chklovski and Pantel, 2004; Torisawa, 2006), predicate phrase (simple sentence) is a reasonable unit because it approximately corresponds to the meaning of single event.
Combination of words and a variety of construction coerce us into handling an enormous numberof expressions than word-based approaches. One
may think taking phrase is like treading a thorny
path because one of the arguments in Section 1 isabout coverage. On this issue, we speculate that
one of the feasible approach to realize a robust system is to divide phenomena into compositional andnon-compositional (idiosyncratic) ones1 , and separately develop resources to handle them as describedin (Fujita and Inui, 2005).
To compute semantic equivalence of idiosyncraticparaphrases, pairs or groups of paraphrases have to
be statically compiled into a dictionary as wordbased thesaurus. The corpus-based approach is valuable for that purpose, although they are not guaranteed to collect all idiosyncratic paraphrases. On theother hand, compositional paraphrases can be captured by a relatively small number of rules. Thus it
seems tolerable approach to generate them dynamically by applying such rules. Our work is targetedat compositional paraphrases and the system can becalled dynamic phrasal thesaurus. Hereafter, we
refer to paraphrases that are likely to be explained
compositionally as syntactic variants.
2.2

Target language: JapaneseWhile the discussion above does not depend on particular language, our implementation of dynamicphrasal thesaurus is targeted at Japanese. Several methods for paraphrasing Japanese predicate
phrases have been proposed (Kondo et al., 1999;
Kondo et al., 2001; Kaji et al., 2002; Fujita et al.,2005). The range they treat is, however, relatively
narrow because they tend to focus on particular paraphrase phenomena or to rely on existing resources.
On the other hand, we define the range of phenomena from a top-down viewpoint. As a concrete definition of predicate phrase in Japanese,noun phrase + case marker + predicate
is employed which is hereafter referred to “phrase.”
Noun phrase and predicate in Japanese themselves subcategorize various syntactic variants asshown in Figure 1 and paraphrase phenomena forabove phrase also involve those focused on their interaction. Thus the range of phenomena is not sonarrow, and intriguing ones, such as shown in examples2 (2) and (3), are included.
1
We regard lexical paraphrases (e.g., “scope” ⇔ “range”)
and idiomatic paraphrases (e.g., “get the sack” ⇔ “be dismissedfrom employment”) as idiosyncratic.
2
In each example, “s” and “t” denote an original sentence
and its paraphrase, respectively. S MALL C APS strings indicate
the syntactic role of their corresponding Japanese expressions.
[N] indicates a nominalizer.

(2) Head switchings. kakunin-o isogu.

8
8
<
>
>
>
>
formal
noun
>
>
:
>
>
>
8
>
>
>
>
>
>
<
>
>
>
noun phrase
>
>
<
>
>
>
> content
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
:
:

checking-ACC to hurry-PRES
We hurry checking it.

t. isoide

kakunin-suru.

in a hurry to check-PRES
We check it in a hurry.

(3) Noun phrase ⇔ sub-clauses. kekka-no saigensei-o

result-GEN reproducibility-ACC to validate-PRES
We validate its reproducibility.

t. [ kekka-o saigen-dekiru ]ka-douka-opredicatekenshou-suru.

[N]-whether-ACC to validate-PRES
We validate whether it is reproducible.

We focus on syntactic variants at least one side ofwhich is subcategorized into the definition of phrase
above. For the sake of simplicity, we hereafter represent those expressions using part-of-speech (POS)
patterns. For instance, (2s) is called N : C : V type,and (3s) is N1 : no : N2 : C : V type.

3 Paraphrase generation system
Given a phrase, the proposed system generates its
syntactic variants in the following four steps:
1. Morphological analysis
2. Syntactic transformation
3. Surface generation with lexical choice
4. SLM-based filtering
where no particular domain, occasion, and media isassumed3 . Candidates of syntactic variants are firstover-generated in step 2 and then anomalies among
them are filtered out in steps 3 and 4 using rule-based
lexical choice and statistical language model.
The rest of this section elaborates on each component in turn.
3.1

Morphological analysis

Technologies of morphological analysis in Japanese
have matured by introducing machine learning techniques and large-scale annotated corpus, and there
are freely available tools. Since the structure of input
phrase is assumed to be quite simple, employment ofdependency analyzer was put off. We simply use amorphological analyzer MeCab4 .
3This corresponds to the linguistic transformation layer of
K URA (Takahashi et al., 2001).
4
http://mecab.sourceforge.net/

compoundmodified

8
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
verb phrase
>
>
>
>
>
<

kenshou-suru.

result-ACC to reproduce-to be able

“koto”“mono”“no”
single word j

>
>
>
>
>
>
>
>
>
>
Adj
>
>
>
>
>
Adjectival verb + “da”
>
>
>
>
>
: Adv + “da”
CopulaN1 N2
N + suffixes
8
N1 + “no” + N2><
Adj + N>
: Adjectival verb + N
clause + N

8
8
>
>
>
>
>
>
<
>
>
>
>
single
word
>
>
>
>
>
<
>
:
8
>
>
>
>
>
<
>
>
>
>
compound
>
>
>
:
:
j
single word
compoundoriginal verb
Sino-Japanese verb
lexical compoundlight verb
Adv + “suru”
original + original
Sino + original
Sino + Sino
N + Sino

Figure 1: Classification of syntactic variants of nounphrase and predicate in Japanese.
Our system has a post-analysis processing. If either of Sino-Japanese verbal nouns (e.g., “kenshou(validation)” and “kandou (impression)”) or transliteration of verbs in foreign language (e.g., “doraibu(to drive)” and “shifuto (to shift)”) is immediately
followed by “suru (to do)” or “dekiru (to be able),”these adjacent two morphemes are joined into a single morpheme to avoid incorrect transformation.
3.2

Syntactic transformation

The second step over-generates syntactic variants
using the following three sorts of knowledge:(i) Transformation pattern: It gives skeletons ofsyntactic variants. Each variant is represented
by POS symbols designating the input constituents and triggers of the generation function
and lexical function below.
(ii) Generation function: It enumerates different
expressions that are constituted with the same
set of words and subcategorized into the required syntactic category. Some of generation
functions handle base phrases, while the rest
generates functional words. Base phrases theformer generates are smaller than that transformation patterns treat. Since some functional
words are disjunctive, the latter generates all
candidates with a separator “/” and leaves theselection to the following step.

Table 1: Grammar in Backus-Naur form, example, and instantiation for each knowledge.
Knowledge type(i) Transformation
pattern

Grammar / Example / Instantiation<transformation pattern> ::= <left pattern> ⇒ <right pattern><left pattern> ::= (<POS symbol>|<word form>)+<POS symbol> ::= (N |C|V |Adj|Adv)<word form> ::= (<hiragana>|<katakana>|<kanji>)+<right pattern> ::=(<POS symbol>|<word form>|<function definition>|<lexical function>)+(a) N : C : V ⇒ adv (V ) : vp(N )(b) N1 : no : N2 : C : V ⇒ N1 : genCase() : vp(N2 ) : ka-douka : C : V(a) kakunin : o: isogu ⇒ adv (isogu) : vp(kakunin)checking
ACC
to hurryadv (to hurry) vp(checking)(b) kekka : no : saigensei: o: kenshou-suru
resultGEN
reproducibility
ACC
to validate-PRES⇒ kekka : genCase() : vp(saigensei)(ii) Generation
function⇒ { shukka
starting fire

, gen-in , shukka
reasonvp(shukka)(iii) Lexicalfunction: ka-douka: o: kenshou-suru

resultcase marker
vp(reproducibility) [N]-whether
ACC
to validate-PRES<generation function> ::= <function definition> ⇒ ’{’<right pattern>+’}’<function definition> ::= <syntactic category>’(’<POS symbol>*’)’<syntactic category> ::= (np | vp | lvc)(a) vp(N ) ⇒ {v (N ) : genVoice() : genTense ()}(b) np(N1 , N2 ) ⇒ {N1 , N2 , N1 : N2 , N1 : no : N2 , vp(N1 ) : N2 , wh(N2 ) : vp(N1 ) : ka, . . .}(a) vp(kakunin)⇒ { v (kakunin) : genVoice(): genTense()}
vp(verification)v (verification) verbal suffix for voice verbal suffix for tense(b) np(shukka, gen-in)np(starting fire, reason)starting fire

: gen-in , shukka
reasonstarting fire

: gen-in , wh(gen-in) : vp(shukka)

: no
GEN

: gen-in ,reason: ka , . . . }vp(starting fire) reason
wh(reason)
vp(starting fire) [N]<lexical function> ::= <relation>’(’<POS symbol>’)’<relation> ::= (n | v | adj | adjv | adv | wh)(a) adv (V )(b) wh(N )„«(given by a verb–adverb dictionary)(a) adv (isogu)⇒ isoideadv (to hurry) „in a hurry«(b) wh(gen-in)⇒ { naze , doushite } (given by a noun–interrogative dictionary)wh(reason)whywhy(iii) Lexical function: It generates different lexical items in certain semantic relations, suchas derivative form, from a given lexical item.
The back-end of this knowledge is a setof pre-compiled dictionaries as described in
Section 4.2.
Table 1 gives a summary of grammar in BackusNaur form, examples, and instantiations of eachknowledge. Figure 2 illustrates an example ofknowledge application flow for transforming (4s)into (4t), where “:” denotes delimiter of constituents.
(4) s. “kakunin:o:isogu”
t. “isoide:{kakunin-suru:{φ, reru/rareru, seru/saseru}:{φ, ta/da}}”First, transformation patterns that match to the given
input are applied. Then, the skeletons of syntactic
variants given by the pattern are lexicalized by consecutively invoking generation functions and lexicalfunctions. Plural number of expressions that generation function and lexical function generate are
enumerated within curly brackets. Transformationis ended when the skeletons are fully lexicalized.
In fact, knowledge design for realizing the transformation is not really new, because we have been
inspired by the previous pattern-based approaches.
Transformation pattern is thus alike that in the
Meaning-Text Theory (MTT) (Mel’čuk, 1996), Synchronous Tree Adjoining Grammar (STAG) (Dras,
1999), meta-rule for Fastr (Yoshikane et al., 1999),

kakunin : o : isogu
N

C

⇒

V

N1:N2:C:V+N

Trans. Pat.

N:C:V

adv(V):vp(N)

N:C:V1:V2+V

adv(isogu) : vp(kakunin)

Lex. Func.

Gen. Func.

adv(V)

vp(N)isoide{v(kakunin) : genVoice() : genTense()}Lex. Func.

Gen. Func.

Gen. Func.

v(N)genVoice()

genTense()

kakunin-suru

φ{, reru/rareru, seru/saseru}φ

isoide : {kakunin-suru : {φ{switch V with Adj

Adj:N:C:V

N:C:AdjFigure 3: Derivations of phrase types.

φ, reru/rareru, seru/saseru} : {, ta/da}}and transfer pattern for K URA (Takahashi et al.,2001). Lexical function is also alike that in MTT.
However, our aim in this research is beyond thedesign. In other words, as described in Section 1,
we are aiming at the following two: to develop resources for handling syntactic variants in Japanese,and to confirm if phrasal thesaurus really contributeto computing semantic equivalence.
Surface generation with lexical choice

The input of the third component is a bunch of candidate phrases such as shown in (4t). This component
does the following three processes in turn:
Step 1. Unfolding: All word sequences are generated by removing curly brackets one by one.
Step 2. Lexical choice: Disjunctive words are concatenated with “/” (e.g., “reru/rareru” in (4t)).
One of them is selected based on POS and conjugation types of the preceding word.
Step 3. Conjugation: In the transformation step,
conjugative words are moved to different positions and some of them are newly generated.
Inappropriate conjugation forms are corrected.
3.4

N:C:Adv:V, ta/da}Figure 2: Syntactic transformation (for (2)).

3.3

+Adv
+Adj

N:C:V

SLM-based filtering

In the final step, we assess the correctness of eachcandidate of syntactic variants using a statistical language model. Our model simply rejects candidate
phrases that never appear in a large size of raw text
corpus consisting of 15 years of newspaper articles(Mainichi 1991–2005, approximately 1.8GB). Although it is said that Japanese language has a degreeof freedom in word ordering, current implementation does not yet employ structured language models
because phrases we handle are simple.

4 Knowledge implementation
4.1

Transformation patterns and generation
functions

An issue of developing resources is how to ensure
their coverage. Our approach to this issue is to describe transformation patterns by extending those forsimpler phrases. We first described following threepatterns for N : C : V type phrases which we consider the simplest according to Figure 1.
(5) a. N : C : V ⇒ vp(N )b. N : C : V ⇒ N : genCase() : lvc(V )c. N : C : V ⇒ adv (V ) : vp(N )While the pattern (5c) is induced from example (2),the patterns (5a-b) are derived from examples (6)and (7), respectively.
ukeru(6) s. shigeki-oinspiration-ACC to receive
to receive an inspirationt. shigeki-sareru
to inspire-PASS
to be inspired(7)s. hada-o shigeki-suru
skin-ACC to stimulate
to stimulate skint. hada-ni shigeki-oataeruskin-DAT stimulus-ACC to giveto give skin a stimulus

Regarding the patterns in (8) as the entire set ofcompositional paraphrases for N : C : V typephrases, we then extended them to a bit more complex phrases as in Figure 3. For instance, 10 patterns

Table 2: Transformation patterns.
Target phrase# of patterns
N :C:V3
N1 : N2 : C : V
1010
N : C : V1 : V2
N : C : Adv : V
7
Adj : N : C : V
4
N : C : Adj
3
Total
37

Table 3: Generation functions.
Definition
Syntactic categorynp(N1 , N2 ) noun phrasevp(N )
verb phrasevp(N1 , N2 ) verb phrase
vp(V1 , V2 ) verb phraselvc(V )
light verb construction
genCase() case marker
genVoice() verbal suffix for voice
genTense () verbal suffix for tense
genAspect () verbal suffix for aspect# of returned value
9
1
2
3
1
4
3
2
2

for N1 : N2 : C : V type phrases shown in (8) havebeen described based on patterns in (5), mainly focusing on interactions between newly introduced N1
and other constituents.
(5a)(8) a. N1 : N2 : C : V ⇒ vp(N1 , N2 )b. N1 : N2 : C : V ⇒(5a)
N1 : genCase() : vp(N2 )c. N1 : N2 : C : V ⇒(5a)N2 : genCase() : vp(N1 )d. N1 : N2 : C : V ⇒np(N1 , N2 ) : genCase() : lvc(V ) (5b)e. N1 : N2 : C : V ⇒ N1 : genCase() :(5b)N2 : genCase() : lvc(V )f. N1 : N2 : C : V ⇒ N2 : genCase() :(5b)N1 : genCase() : lvc(V )g. N1 : N2 : C : V ⇒(5c)adv (V ) : vp(N1 , N2 )h. N1 : N2 : C : V ⇒adv (V ) : N1 : genCase() : vp(N2 ) (5c)i. N1 : N2 : C : V ⇒adv (V ) : N2 : genCase() : vp(N1 ) (5c)j. N1 : N2 : C : V ⇒(new)np(N1 , N2 ) : C : V
The number of transformation patterns we have so
far developed is shown in Table 2.
Generation functions shown in Table 3 are developed along with creating transformation patterns.
Although this is the heart of the proposed model,
two problems are remained: (i) the granularity ofeach generation function is determined according toTable 4: Dictionaries for lexical functions.

ID POS-pair|D| |C| |D ∪ C| |J|(a) noun–verb
3,431
3,431 3,431
308 667
906 475 †(b) noun–adjective(c) noun–adjectival verb
1,579
1,579 1,579(d) noun–adverb
271
271 271252
252 192 †(e) verb–adjective(f) verb–adjectival verb
74
74 68 †(g) verb–adverb
74
74 64 †(h) adjective–adjectival verb
66 95
159 146 †(i) adjective–adverb
33
33 26 †(j) adjectival verb–adverb
70
70 70
Total
6,158 762
6,849 6,322

our linguistic intuition, and (ii) they do not ensure ofgenerating all possible phrases. Therefore, we haveto establish the methodology to create this knowledge more precisely.
4.2

Lexical functions

Except wh(N ), which generates interrogatives asshown in the bottom line of Table 1, the relations
we have so far implemented are lexical derivations.
These roughly correspond to S, V, A, and Adv in
MTT. The back-end of these lexical functions is aset of dictionaries built by the following two steps:
Step 1. Automatic candidate collection:
Most
derivatives in Japanese share the beginningof words and are characterized by the correspondences of their suffixes. For example,“amai (be sweet)” and “amami (sweetness)”
has a typical suffix correspondence “∗-i:∗-mi”
of adjective–noun derivation. Using this clue,
candidates are collected by two methods.
• From dictionary: Retrieve all word pairs fromthe given set of words those satisfying thefollowing four conditions: (i) beginning withkanji character, (ii) having different POSs,(iii) sharing at least the first character and thefirst sound, and (iv) having a suffix patternwhich corresponds to at least two pairs.
• Using dictionary and corpus: Generate candidates from a set of words by applying a set of
typical suffix patterns, and then check if eachcandidate is an actual word using corpus. Thisis based on (Langkilde and Knight, 1998).
Step 2. Manual selection: The set of word pairs
collected in the previous step includes those do
not have particular semantic relationship. This
step involves human to discard noises.

Table 4 shows the size of 10 dictionaries, where
each column denotes the number of word pairs retrieved from IPADIC5 (|D|), those using IPADIC,
seven patterns and the same corpus as in Section 3.4(|C|), their union (|D ∪ C|), and those manually judged correct (|J|), respectively. The sets ofword pairs J are used as bi-directional lexical functions, although manual screening for four dictionaries without dagger (†) are still in process.

5 Discussion
5.1

Unit of processing

The working hypothesis underlying our work is thatphrase is the appropriate unit for computing semantic equivalence. In addition to the arguments in
Section 1, the hypothesis is supported by what isdone in practice. Let us see two related fields.
The first is the task of word sense disambiguation (WSD). State-of-the-art WSD techniques refer
to context as a clue. However, the range of context
is usually not so wide: words and their POSs withinsmall window centered the target word and content
words within the same sentence of the target word.
The task therefore can be viewed as determining themeaning of phrase based on its constituent words
and surrounding content words.
Statistical language model (SLM) is another field.
SLMs usually deal with various things within word
sequence (or structure) at the same time. However, relations within a phrase should be differentiated from that between phrases, because checking
the former is for grammaticality, while the latter forcohesion. We think SLMs should take the phrase todetermine boundaries for assessing the correctnessof generated expressions more accurately.
5.2

Compositionality

We examined how large part of manually createdparaphrases could be generated in our compositional
approach. First, a set of paraphrase examples werecreated in the following procedure:
Step 1. Most frequent 400 phrases typed N1 : N2 :C : V were sampled from one year of newspaper articles (Mainichi 1991).
Step 2. An annotator produced paraphrases for eachphrase. We allowed to record more than one
5

http://mecab.sourceforge.jp/

paraphrase for a given phrase and to give up
producing paraphrases. As a result, we obtained 211 paraphrases for 170 input phrases.
Manual classification revealed that 42% (88 / 211)of paraphrases could be compositionally explainable, and the (theoretical) coverage increases to 86%(182 / 211) if we have a synonym dictionary. Thisratio is enough high to give these phenomena preference as the research target, although we cannot reject a possibility that data has been biased.
5.3

Sufficient condition of equivalence

In our system, transformation patterns and generation functions offer necessary conditions for generating syntactic variants for given input. However,
we have no sufficient condition to control the application of such a knowledge.
It has not been thoroughly clarified what clue canbe sufficient condition to ensure semantic equivalence, even in a number of previous work. Though,at least, roles of participants in the event have to bepreserved by some means, such as the way presentedin (Pantel et al., 2007). Kaji et al. (2002) introduced
a method of case frame alignment in paraphrase generation. In the model, arguments of main verb in thesource are taken over by that of the target according
to the similarities between arguments of the source
and target. Fujita et al. (2005) employed a semantic
representation of verb to realize the alignment of therole of participants governed by the source and target verbs. According to an empirical experiment in(Fujita et al., 2005), statistical language models do
not contribute to calculating semantic equivalence,but to filtering out anomalies. We therefore plan toincorporate above alignment-based models into oursystem, for example, within or after the syntactictransformation step (Figure 2).
5.4

Ideas for improvement

The knowledge and system presented in Section 3
are quite simple. Thus the following features should
be incorporated to improve the system in addition tothe one described in Section 5.3.
• Dependency structure: To enable flexible
matching, such as Adv : N : C : V type input
and transformation pattern for N : C : Adv :V type phrases.
• Sophisticated SLM: The generation phaseshould also take the structure into account toevaluate generated expressions flexibly.
• Knowledge development: Although we havenot done intrinsic evaluation of knowledge, we
are aware of its incompleteness. We are continuing manual screening for the dictionaries
and planning to enhance the methodology ofknowledge development.

6 Conclusion
To enhance the technology for computing semantic equivalence, we have introduced the notion ofphrasal thesaurus, which is a natural extension of
conventional word-based thesaurus. Plausibility of
taking phrase as the unit of processing has been discussed from several viewpoints. On the basis ofthat, we have been developing a system to dynamically generate syntactic variants in Japanese predicate phrases which utilizes three sorts of knowledge
that are inspired by MTT, STAG, Fastr, and K URA.
Future work includes implementing more precisefeatures and larger resources to compute semantic
equivalence. We also plan to conduct an empiricalevaluation of the resources and the overall system.

Acknowledgments
This work was supported in part by MEXT Grantsin-Aid for Young Scientists (B) 18700143, and for
Scientific Research (A) 16200009, Japan.

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