Preferential Presentation of Japanese Near-Synonyms
Using Definition Statements
Hiroyuki OKAMOTO Kengo SATO Hiroaki SAITO
Department of Information and Computer Science
Keio University
3–14–1 Hiyoshi, Kouhoku-ku, Yokohama 223–8522, Japan
Tel: (+81–45)563–1151 (ex 43250), Fax: (+81–45)566–1747
fmotch, satoken, hxsg@nak.ics.keio.ac.jp
Abstract
This paper proposes a new method of
ranking near-synonyms ordered by their
suitability of nuances in a particular con-
text. Our method distincts near-synonyms
by semantic features extracted from their
definition statements in an ordinary dictio-
nary, and ranks them by the types of fea-
tures and a particular context. Our method
is an initial step to achieve a semantic
paraphrase system for authoring support.
1 Introduction
Most researches on automatic paraphrasing aim ei-
ther at document modification for a wide range
of NLP applications (Shirai et al., 1998; Tomuro
and Lytinen, 2001), at reading comprehension sup-
port (Inui and Yamamoto, 2001), or at transforma-
tion based on external constraints (Dras, 1998). On
the other hand, authoring / revision support is known
as another type of paraphrasing which targets at texts
in preparation. However, there are not so many re-
searches of such paraphrasing.
Paraphrase systems which aim at revising docu-
ments can be classified into three types:
† Syntactic suitability
This type of systems points out spelling or
grammatical mistakes and corrects them, such
as a grammar checker (Heidorn, 2000).
† Readability
Similar to reading comprehension support,
this type of paraphrase systems aims to
simplify difficult / complicated sentences or
phrases (Suganuma et al., 1990; Inui and
Okada, 2000).
† Semantic suitability
To reflect authors’ intentions precisely, these
paraphrase systems replace words, which are
semantically ambiguous or inadequate, to ones
which are suitable for their contexts.
Almost all known authoring / revision support sys-
tems aim at syntactic suitability or readability, while
researches of the third type of paraphrasing, which
handle semantics, are very rare.
Let us consider a kind of authoring support sys-
tem, which first presents near-synonyms (words
counted among the same semantic category) of a
target word in an input sentence. Then, based on
user’s choise, the system paraphrases the target word
to the selected one with keeping syntactic and se-
mantic consistency through paraphrasing. Espe-
cially for semantic consistency, it is important to
express semantic differences between paraphrased
word pairs clearly. If fine-grained meanings of all
near-synonyms (not only a paraphrased pair) can be
extracted at a time, the system would be able to
present semantically suitable near-synonyms. Based
on this idea, this paper proposes a new method of
ranking Japanese near-synonyms ordered by their
suitability of nuances in a particular context. First,
this paper describes an overview of the method in
Section 2. Next, Section 3 shows the classification
of fine-grained meanings of a word and a method
of extracting those fine-grained meanings from a
definition statement of the word, to identify se-
mantic differences between near-synonyms. Then,
Section 4 presents our method of ranking near-
synonyms using fine-grained meanings described in
Section 3. Finally, this paper shows conclusion and
further works in Section 5.
2 Overview of our method of preferential
presentation
Though some word processing applications (e.g.
Microsoft Word) have a function of showing near-
synonyms of a word, it is not easy to choose the
most adequate word from the near-synonyms be-
cause they are not ordered by their semantic similar-
ity or suitability. Also, a simple replacement from
a word to one of its near-synonyms is very danger-
ous, because there are some differences between the
words in their modification rules and in their fine-
grained meanings.
Against these semantic problems, we propose a
new method of presenting near-synonyms ordered
by their semantic suitability in a particular context.
When a target word is given from an input sentence,
first our method obtains all near-synonyms of the
target word from an existing thesaurus, and differen-
tiates them semantically by features extracted from
their definition statements. Next, our method ranks
those near-synonyms by relations between the type
of features and the context of the input sentence. Fi-
nally, the ranking of near-synonyms are presented
with information of variation in the original sen-
tence for each near-synonym. This process enables
the user to choose a word suitable for the input con-
text, and helps prevention of semantic variation (or
redundancy / loss) in paraphrasing.
3 Semantic differentiation between
near-synonyms
As the first step to realize the preferential suggestion
of near-synonyms, we identify fine-grained word
senses of near-synonyms in order to differentiate
them semantically, by using sentences written in an
ordinal dictionary (definition statements) and word
co-occurrence information extracted from large cor-
pora.
3.1 Fine-grained word senses
There are some researches which deal with fine-
grained word senses for a lexical choice in language
generation (DiMarco et al., 1993; Edmonds, 1999).
Edmonds roughly classified semantic differences
between near-synonyms into four categories: deno-
tational (difference in nuances of near-synonyms),
expressive (in attitudes or emotions), stylistic (in
formalities or dialects), and collocational (as idioms
or in co-occurrence restrictions). In addition, he
classified them into 35 types and proposed an on-
tology for describing their differences formally.
Edmonds implemented I-Saurus, a prototype im-
plementation of this ontology, to achieve a lexical
choice in machine translation and denoted the effec-
tiveness of differences between near-synonyms for
a lexical choice. Though, there is a crucial prob-
lem that he did not mention how to obtain those dif-
ferences automatically. Against this problem, our
method extracts such differences by using definition
statements for each near-synonym. Although (Fu-
jita and Inui, 2001) has already focused on using
definition statements in order to determine a pair of
near-synonyms whether one can be paraphrased to
the other or not, it was only a kind of matching be-
tween two statements and did not identify individ-
ual features in each statement. Therefore, this paper
defines three types of semantic features as follows,
which can be extracted from definition statements:
† Core meaning indicates the basic sense of a
word. All near-synonyms in a category must
always have the same core meaning, such as
the name of the category which they belong to.
† Denotation, which can be paraphrased to ‘nu-
ance’, is defined as “the thing that is actually
described by a word rather than the feelings or
ideas it suggests” in Longman web dictionary1.
In this paper, this feature is defined as a mean-
ing included in a word, which partially qualify
the core meaning. It is similar to a denotational
constraint in (Edmonds, 1999).
† Lexical restriction of a word is a constraint on
the range of co-occurrence of the word. This
feature is almost the same as a collocational
constraint in (Edmonds, 1999).
An example of these features is shown in Figure 1.
We divide our method into two steps to extract
each feature from a definition statement. First, we
extract a word defined as a core meaning and all
other content words (in Section 3.2). Then, the ex-
tracted words except the core meaning are classified
into denotations or lexical restrictions by using each
co-occurrence information obtained from large cor-
pora (in Section 3.3).
1http://www.longmanwebdict.com/
Word:
^M\�
6Psaikon
(rebuilding of shrines / temples)
Definition statement:
�
�~���Po�b\q{�
jinja (shrine) bukkaku (temple) wo
(OBJ) tate (to build) naosu (to repair)
koto (matter)
(To build a shrine or a temple to repair.
Core meaning:Potate (build)
Denotation:�bnaosu (repair)
Lexical restriction:
�jinja (shrine)
��bukkaku (temple)
Figure 1: Features in a definition statement
3.2 Extraction of fine-grained word senses
In this paper, we assume that a definition statement
of a word (hereafter an entry) in a dictionary con-
sists of four types of materials as follows:
† Core meaning is a word which exactly de-
scribes a particular semantic category which
the entry belongs to.
† Fine-grained meaning semantically differen-
ciates the entry from its near-synonyms. It
is defined as a core meaning of some content
words in the definition statement. Fine-grained
meaning can be divided into “denotation” or
“lexical restriction”.
† Stop word indicates a content word which
commonly and frequently appears in any def-
inition statement.
† Others include function words and symbols.
According to this assumption, the “core mean-
ing” and “fine-grained meanings” of an entry are
extracted from a definition statement, using of
Kadokawa thesaurus (Ohno and Hamanishi, 1981)2.
A procedure of this method is given as follows:
Step 1. For each morpheme in the morpheme dic-
tionary of ChaSen (Matsumoto et al., 2002),
a Japanese morphological analyzer, add a
label of a semantic category in Kadokawa
Thesaurus, which the morpheme belongs to.
Step 2. Assign semantic labels to each morpheme in
a definition statement of an entry e, by ap-
plying ChaSen to the statement.
2Kadokawa thesaurus semantically categorizes 57,130 en-
tries into 2,924 categories and each entry has a definition state-
ment.
Step 3. Give a word c as a “provisional” core mean-
ing if c is classified into the same semantic
category as e.
Step 4. Extract all semantic labels, which are as-
signed to all content words except c, as fine-
grained meanings.
Step 5. Recursively apply Step 2–4 to the definition
statement of c until no core meaning is ex-
tracted from the definition statement.
Step 6. Define c extracted at last as the “true” core
meaning of e.
According to this procedure, some fine-grained
meanings could be extracted from stop words. Thus,
we give a semantic weight to each fine-grained
meaning, by the reciprocal of its occurrence prob-
ability in all definition statements. These weights
can distinct true fine-grained meanings from ones
extracted from stop words.
A result of this method is shown in Figure 2,
where the bold numbers show their categories and
the italics show their weights.
Word: [394]
^M\�
6Psaikon
(rebuilding of shrines / temples)
Core meaning:
[394]Po�tateru (to build)
Fine-grained meaning:
[727a]
�jinja (shrine: 5687)
[940c]��bukkaku (temple: 6184)
[277b]�bnaosu (to alter: 1441)
[277c]�bnaosu (to recover: 2359)
[392]�bnaosu (to repair: 7494)
[417a]�bnaosu (to get right: 3703)
[811]\qkoto (matter: 30)
Figure 2: Example of extraction of core-meaning
and fine-grained meanings
3.3 Classification of fine-grained word senses
After obtaining features in Section 3.2, our method
classifies fine-grained meanings into denotations
and lexical restrictions, according to the following
heuristics:
† If a word w includes a denotation d, w seldom
co-occurs with any word whose core meaning
is d. For example, one possible paraphrase of a
sentence
He is extremely angry.
is
He is enraged.
where the word extremely is deleted, because
enraged has a denotation “extremely” if angry
is defined as the core meaning of enraged.
† If w involves a lexical restriction l, w often co-
occurs with words whose core meaning is l. For
example, “a rancid butter” is more appropriate
than “a rotten butter”, because rancid has a lex-
ical restriction “oily or fatty food”, while rotten
does not.
Based on these heuristics, our method classifies fine-
grained meanings of an entry as follows:
Step 1. Assign semantic labels to all words in cor-
pora (consisting of 1.93 million sentences,
including newspapers 3 and novels 4).
Step 2. Obtain co-occurrence frequencies of all
pairs between a word and a semantic label
of a neighbor word from the corpora.
Step 3. Delete the entry e from the thesaurus if e
does not appear in the corpora at all.
Step 4. For each fine-grained meaning f of e which
belongs to a semantic category C, compute
co-occurrence probabilities
P(f;C) =
P
i nsifP
i Nsi
(1)
P(f;e) = nefN
e
(2)
where si is a near-synonym of e, nab is the
co-occurrence frequency between a word a
and a label b, and Na is the frequency of a.
Step 5. Remove f if P(f;C) = 0.
Step 6. Define f as a denotation if P(f;e) = 0. The
weight of the denotation is the product of
P(f;C) and the weight of f.
Step 7. Define f as a lexical restriction if P(f;e) 6=
0. The weight of the lexical restriction is the
product of P(f;e)P(f;C) and the weight of f.
Figure 3 shows an example of classification about
the word ‘saikon (6P)’. In Figure 3, under-lined
features are the results of word sense disambiguation
and elimination of stop words.
3Mainichi Shimbun CD-ROM
http://cl.aist-nara.ac.jp/lab/resource/
cdrom/Mainichi/MS.html
4Aozora Bunko http://www.aozora.gr.jp/
Word: [394]
^M\�
6Psaikon
(rebuilding of shrines / temples)
Denotation:
[277b]�bnaosu (to alter: 1.45)
[392]�bnaosu (to repair: 4.19)
Lexical restriction:
[727a]
�jinja (shrine: 8518)
[940c]��bukkaku (temple: 5859)
[277c]�bnaosu (to recover: 3504)
[417a]�bnaosu (to get right: 2135)
[811]\qkoto (matter: 15)
Figure 3: Classification example of fine-grained
meanings
3.4 Evaluation and discussions
We applied these procedures to all 57,130 entries in
Kadokawa thesaurus (2,924 categories). As a result,
36,434 entries, which consist of one core meaning
and 0 or more fine-grained meanings, and 1,857 en-
tries, which has no core meaning but is refered as a
core meaning to other entries, were obtained. One
entry has 4.7 denotations and 5.1 lexical restrictions
on average.
To evaluate our methods, we compared the results
of automatic extraction against manually extracted
ones for randomly selected 50 entries. Table 1 shows
the result of extracting core meanings, and the result
of the classification is shown in Table 2.
number of entries
corrects 40
errors 10
(direct) (4)
(indirect) (6)
precision 80 %
Table 1: Result of extracting core meanings
Failure results of extractions of core meanings ap-
peared in the following cases; a core meaning in
a definition statement does not belong to the same
semantic category as the entry; the correct core
meaning involves negative expressions in a defini-
tion statement; or two or more near-synonyms are
appeared in one definition statement. Therefore, the
extraction of core meanings needs to be estimated
without relying on their semantic categories, that
is, with other information such as modification re-
result recall [%]
denotation lexical restriction
answer denotation 56 13 81.2lexical restriction 22 20 47.6
precision [%] 71.8 60.6
Table 2: Result of classification
lations of a definition statement.
Table 2 shows that both the precision and the
recall of the classification into lexical restrictions
are worse than the ones of denotations. A sparse
data problems could cause it. In our classification
method, if a feature of an entry does not co-occur
with the entry, the feature is classified into a denota-
tion or deleted, even though it is expected to be de-
fined as a lexical restriction. It would be improved
by increasing domains and the size of corpora, or by
using information of modification relations just as
the extraction of core meanings.
4 Preferential presentation of
near-synonyms
We secondly propose a method of ranking near-
synonyms by using information derived in Sec-
tion 3. Though (Edmonds, 1999) proposed a ranking
method for lexical choice by using information of
fine-grained meanings in I-Saurus, it requires more
detailed information than the one which can be ex-
tracted from a definition statement. Thus, this pa-
per proposes a ranking method as follows: when
a target word in a sentence is given, our method
obtains all near-synonyms5 of the target word and
their semantic features. Then, our method ranks the
near-synonyms with respect to their suitability be-
tween the input context and features of each near-
synonym. Additionally, if a paraphrase to a near-
synonym causes neighbor words in the input sen-
tence to arrange in order to keep semantic consis-
tency, our method adds such information to the near-
synonym when the ranking is presented.
4.1 Comparison between denotations and
contexts
“Denotations” can appear in any word, including
a target word in an input sentence. Therefore, all
5There are sometimes two or more core meanings in one
semantic category. We treat whole core meanings as the exactly
same meaning here.
denotations of each near-synonym have to be com-
pared not only with the input context but with deno-
tations of a target word. Our method determines the
propriety of paraphrasing between a target word w
and its near-synonym si for each denotation dij of
si, with the following cases:
Case 1. No denotation appears in neither w nor si:
)w can be directly paraphrased to si.
Case 2. w has a denotation dw equivalent to dij:
) w can be paraphrased to si on the sense
of dij.
Case 3. dw does not match with any dij:
) w can be paraphrased to si with adding
dw to the input sentence.
Case 4. dij does not match with any dw:
(a) if dij can be covered with a neighbor
word w0 of w in the input sentence:
)w can paraphrase to si with deleting
w0 from the input sentence.
(b) if dij can not be covered with any words
in the input sentence:
)w can not be paraphrased to si.
In Case 3 and Case 4a, some arrangements (addi-
tion / deletion of words) to the input sentence are
needed. Our method presents these information with
the presentation of near-synonyms rankings (in Sec-
tion 4.3).
According to these cases, the total denotational
score Sd of si is defined by
Sd = X
j
pWj (3)
where Wj is the weight of dij (one of the denota-
tions of si) and
p =
8>
<
>:
1 (in Case 1, 2, 4a)
0 (in Case 3)
¡1 (in Case 4b)
Note that Case 3 gives no weight, because the case
does not consider any denotation of si but compares
only between dw and its context.
4.2 Comparison between lexical restrictions
and contexts
“Lexical restriction”, the other fine-grained mean-
ing, is the feature which notably often co-occur with
its target word, as described in Section 3.3. In fact,
however, a word which often co-occurs with a target
word does not have to belong exactly to one of the
lexical restrictions of the target word. They could
be the “similar” words. Therefore, it is necessary to
compute the similarity between a lexical restriction
and a context in order to compare them.
The thesaurus used in our method has a tree struc-
ture and each entry belongs to the node at 4 or 5 in
depth. The similarity can be defined by a heuristic
approach that any two words are semantically inde-
pendent if the depth of their root node is less than
3, such as the categories between [588] “rebels” and
[506] “private and public”. Hence, our method de-
fines the similarity between a lexical restriction vi
and a semantic label qi of a word in an input context
as follows:
sim(vi;qi) = log2
 dep(root(v
i;qi))£4
dep(vi)+dep(qi)
¶
(4)
where root(a;b) is the root node of the minimum
subtree which includes both a and b, and dep(a) is
the depth of a in the thesaurus.
To determine the score of a lexical restriction,
there is another problem. An input sentence has sev-
eral content words outside of the target word, and
some of them belong to several semantic categories
because of their ambiguities. Also, the target word
often has two or more lexical restrictions. Thus,
each lexical restriction must select a semantic label
which has the highest similarity with the lexical re-
striction from the input sentence. Against the prob-
lem, first, our method computes the similarities of all
possible pairs which consist of a lexical restriction
and a semantic label extracted from the sentence.
Then, our method extracts pairs in descending or-
der of the similarity with no overlap in any category
or any lexical restriction.
Based on this process, we can compute the total
score Sv of each near-synonym si of a target word
w in an input sentence, with all extracted pairs of a
lexical restriction vj and a semantic label qj in the
input sentence by
Sv = X
j
(Wj ¢sim(vj;qj)) (5)
where Wj is the weight of vj.
4.3 Ranking method
This section describes our method of ranking near-
synonyms with respect to the scores defined in Sec-
tion 4.2 and Section 4.1, which is the aim of this
paper. The criterion of ranking is simply the sum of
normalized Sd and Sv6. Our method presents near-
synonyms according to their ranking, and if neces-
sary, information of arrangements to an input sen-
tence (extracted in Section 4.1) are shown with each
near-synonym.
4.4 An example
When an input sentence is
���Po�b{�
tera (joss house) wo (OBJ) tate (to build)
naosu (to repair)
(Someone rebuilds a joss house.)
and the word “Po(�) (tate(ru), to build)” is given
as a target, the semantic labels assigned to each con-
tent word in the sentence are
�tera [727b] temple
Potate [394] to build
�bnaosu [277b] to alter [277c] to recover
[392] to repair [417a] to get right
and 24 near-synonyms of tateru are extracted. Then,
our method computes Sd and Sv for each near-
synonym. For example, the scores of a word “
^M\�
6P
(saikon, rebuilding of shrines / temples)”, which in-
cludes features shown in Figure 3, are given as fol-
lows:
† Sd (the denotational score)
For the denotations of saikon, [277b] (to al-
ter: 1.45) and [392] (to repair: 4.19) could be
obtained, where the italic numbers show their
weight. They match to the labels in the word
naosu, thus Sd of saikon is 5.64 and the word
naosu is given as a deletion information.
† Sv (the score in lexical restriction)
For the lexical restrictions of saikon, [277c] (to
recover: 3504), [417a] (to get right: 2135),
[727a] (shrine: 8518), [811] (matter: 15) and
[940c] (temple: 5859) could be obtained, then
the extracted pairs and their similarity are cal-
culated as follows:
6Each score has to be normalized because the place of Sd
far differs from that of Sv.
lexical
context similarityrestriction
[277c] , [277c] 1:00
[417a] , [417a] 1:00
[727a] , [727b] 0:68
[811] , [392] ¡1:00
[940c] , [277b] ¡1:32
Therefore, Sv of saikon is calculated as 3682.
Finally, by computing Sd and Sv of all the
other near-synonyms, our method ranks the near-
synonyms and presents them as shown in Figure 4.
In Figure 4, the first 9 near-synonyms can be para-
phrased from the target word appropriately. How-
ever, saikon is ranked next to fushin contrary to our
expectation that it would be ranked as the first, be-
cause saikon and the fifth word saiken has the same
orthography, and thus the co-occurrence information
of saikon is imprecise by mixture with the informa-
tion of saiken.
4.5 Evaluation and discussions
To evaluate our ranking method, we randomly ex-
tracted 40 sentences from corpora and applied our
method to a certain word in each sentence. Also, for
each case, we manually selected all near-synonyms
which can be paraphrased7. We evaluated the rank-
ing results of our method by the measure of non-
interpolated average precision (NAP):
NAP = 1R
nX
i=1
zi
i
ˆ
1+
i¡1X
k=1
zk
!
(6)
where R is the number of near-synonyms which can
be paraphrased, n is the number of presented near-
synonyms, and
zi =
8<
:
1 if a near synonym in rank i can be
paraphrased
0 otherwize
Table 3 shows the result.
Table 3 shows that our method is remarkably ef-
fective for the judgement of semantic suitability of
near-synonyms if a target word is not ambiguous.
However, the average precision is worse for ambigu-
ous words, thus it is important to disambiguate those
target words before applying to our method.
7For the criterion if a word can paraphrase to another or not,
we dissemble any addition / deletion informations. That is, we
assume that a word can paraphrase if the paraphrased sentence
has the same meaning as the original with some changes to their
context.
ambiguity of NAP [%]
target word our method non-
(sentences) Sd Sv Sd +Sv ordered
distinct (21) 74.2 63.8 71.2 60.0
vague (19) 48.8 48.3 51.0 42.1
both (40) 62.8 56.9 62.2 52.0
Table 3: Average precision of ranking
Most of failure results are caused by the follow-
ing cases; incorrect core meanings or fine-grained
meanings were extracted in Section 3; adequate re-
lations between a near-synonym and an input con-
text could not be identified because of the ambiguity
of neighbor words in the input sentence; or the se-
mantic range of the label of a denotation or a lexi-
cal restriction is too wide to express the fine-grained
meaning of the near-synonym clearly.
In addition, Table 3 shows that the average preci-
sion by only Sv is worse than the one by only Sd. It
could be caused by the low precision of classifica-
tion into lexical restrictions and by the inadequacy
in the measure of similarity described in Section 4.2.
To improve those problems, another measure such as
semantical similarities without using a structure of a
thesaurus is needed. Also, we would learn from a
method of lexical choice with knowledge about col-
locational behavior (Inkpen and Hirst, 2002).
Though we have not discussed the evaluation of
the propriety of arrangements to an input sentence,
it seems that the information of addition often occurs
imprecisely, against that the information of deletion
appears infrequently but almost correctly, because,
in our method, all denotations of a target word are
given as the information of addition when they do
not match with any denotation of a near-synonym.
Therefore, we must define the importance of each
addition information and to present selected ones.
5 Conclusion and future work
This paper proposed a new method of preferential
presentation of Japanese near-synonyms in order to
treat with semantic suitability against contexts, as a
first step of semantic paraphrase system for elabo-
ration. We achieved the effectiveness of using def-
inition statements for extracting fine-grained mean-
ings, especially for denotations. Also, the experi-
mental results showed that our method could rank
near-synonyms of an unambiguous word for 71%
1.�
efushin (_	�:�b) (delete naosu) 6.�chikuzo
(Construct or repair a house / a temple / a road) (Build or construct)
2.
^M\�
6Psaikon (_	�:�b) (delete naosu) 7.Po�tateru
(Rebuild a shrine / a temple) (Build)
3.	.�shuchiku (_	�:�b) (delete naosu) 8.�Xkizuku
(Repair a house etc.) (Build)
4.Pqkonryu 9.Pkenzo
(Build a chapel / a tower of a temple) (Construct a buildiing / a ship)
5.
^MZ�
6Psaiken 10.Po
�`tatemashi
(Rebuild or Reconstruction) (Add to a building)
Figure 4: Result of preferential presentation of “tera wo tate naosu.”
in accuracy by non-interpolated average precision,
about 10 points higher than non-ordered.
We have discussed only the initial step of the elab-
oration system, thus one of our future work is to
handle syntactic and semantic constraints on actual
paraphrasings after applying this method.
Acknowledgements
We would like to thank Mainichi Shinbun-sha and
Aozora Bunko for allowing us to use their corpora,
and Kadokawa Sho-ten for providing us with their
thesaurus. We are also grateful to our colleagues for
helping our experiment.

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