Adjective-to-Verb Paraphrasing in Japanese
Based on Lexical Constraints of Verbs
Atsushi Fujita† Naruaki Masuno†† Satoshi Sato† Takehito Utsuro‡†Graduate School of Engineering, Nagoya University††
IBM Engineering & Technology Services, IBM Japan, Ltd.
‡Graduate School of Systems and Information Engineering, University of Tsukuba

Abstract
This paper describes adjective-to-verb
paraphrasing in Japanese. In this paraphrasing, generated verbs require additional suffixes according to their difference
in meaning. To determine proper suffixes
for a given adjective-verb pair, we have examined the verbal features involved in thetheory of Lexical Conceptual Structure.

1 Introduction
Textual expressions that (roughly) convey thesame meaning are called paraphrases. Since generating and recognizing paraphrases has a potential to contribute to a broad range of natural language applications, such as MT, IE, and QA, many
researchers have done a lot of practices on automatic paraphrasing in the last decade.
Most previous studies have addressed paraphrase phenomena where the syntactic category
is not changed: e.g., noun-to-noun (“document”⇔ “article”), verb-to-verb (“raise” ⇔ “bring up”).
In these inner-categorial paraphrasing, only limited types of problems arise when replacing words
or phrases with their synonymous expressions.
On the other hand, this paper focuses on intercategorial paraphrasing, such as adjective-to-verb(“attractive” ⇔ “attract”) that leads to novel typeof problems due to the prominent differences inmeaning and usage. In other words, calculating
those differences is more crucial to determine how
they can or cannot be paraphrased.
The aim of this study is to clarify what lexical
knowledge is required for capturing those differences, and to explore where such a knowledge canbe obtained from. Recent work in lexical semantics has shown that syntactic behaviors and semantic properties of words provide useful information to explain the mechanisms of several classesof paraphrases. More specifically, lexical properties involved in the theory of Lexical Conceptual
Structure (LCS) (Jackendoff, 1990) have seemed

to be beneficial because each verb does not function idiosyncratically. However, in the literature,
there have been less studies for other syntactic
categories than verbs. To the best of our knowledge, the Meaning-Text Theory (MTT) (Mel’čukand Polguère, 1987) is one of the very few frameworks. In MTT, lexical properties and intercategorial paraphrasing are realized with a uniquesemantic representation irrespective of syntactic
categories and what are called lexical functions,e.g., S0 (receive) = reception.
To make out how the recent advances in lexical semantics for verbs can be extended to othersyntactic categories, we assess LCS for intercategorial paraphrasing. We choose adjectives asa counterpart of paraphrasing because they behave
relatively similar to verbs compared with othercategories: both adjectives and verbs have inflection and function as predicates, adnominal elements, etc. Yet, we speculate that their difference
in meaning and usage reveal intriguing generation
problems. To put it briefly, adjective-to-verb paraphrasing in Japanese requires verbal suffixes suchas “ta (past / attributive)” in example (1)1 :(1) s. furui otera-no jushoku-o tazune-ta.
be old temple-GEN priest-ACC to visit-PAST
I visited a priest in the old temple.

t. furubi-taotera-no

jushoku-o tazune-ta.

to olden-ATTR temple-GEN priest-ACC to visit-PAST
I visited a priest in the olden(ed) temple.

2 Preliminary investigation
To make an investigation into the variation anddistribution of required verbal suffixes, we collected a set of paraphrase examples through thefollowing semi-automatic procedure:
Step 1. We handcrafted adjective-verb pairs
based on JCore (Sato, 2004), which classifies
Japanese words into five-levels of readability.
Our 128 pairs (for 85 adjectives) contain only
those sharing first few phonemes (reading)1
For each example, “s” and “t” denote an original sentence and its paraphrase, respectively.

Table 1: Distribution of verbal suffixes used.
Verbal suffixes
Cadc Cpr1 Cpr2
ru91605420tei-ru
1480 wherere-ru
250
re-tei-ru
ru: base formta
57
0
7tei: progressive / perfective
202tei-ta
re: passive / potential
re-ta
601
ta: past / attributive001
re-tei-ta
tea: perfective
400
both ta and tei-ru
both ta and ru
1000
20tea-ru
Total
10073
11

and characters (kanji), and either of adjective
or verb falls into the easiest three levels.
Step 2. Candidate paraphrases for a given sentence collection are automatically generated
by replacing adjectives with their corresponding verbs. Multiple candidates are generated for adjectives that correspond to multiple verbs.
Step 3. The correctness of each candidate paraphrase is judged by two human annotators.
The basic criterion for judgement is that twosentences are regarded as paraphrases if andonly if they share at least one interpretation.
In this step, the annotators are allowed to revise candidates: (i) append verbal suffixes,(ii) change of case markers, and (iii) insert
adverbs. Finally, candidates that both annotators judge correct qualify as paraphrases.
Assuming that the variation and distribution ofverbal suffixes vary according to the usage of adjectives, we separately collected paraphrase examples for adnominal and predicative usages.
Adnominal usages: For 960 sentences randomly
extracted from a one-year newspaper corpus,
Mainichi 1995, we obtained 165 examples for 142
source sentences. We then divided them into twoportions: 12 adjectives that appeared only once
and at least one examples for the other adjectives
were kept unseen (Cado ), while the remaining examples (Cadc ) were used for our investigation.
Predicative usages: For 157 example sentences
within IPAL adjective dictionary (IPA, 1990), wegenerated candidate paraphrases. 84 candidates
for 70 sentences qualified as paraphrases. They
are then divided into two portions according to
the tense of adjectives: Cpr1 consists of examples
where adjectives appear in base form and Cpr2 isfor “ta” form (past tense).
Table 1 shows the distribution of verbal suffixes
used for given adjective-verb pairs in each portionof example collections. We confirmed that theirdistribution was fairly different. In the remaining
sections, we focus on adnominal usages because
examples of predicative usages have displayed adegree of compositionality. Which of “ru” or “ta”
must be used is given by the input: if a given adjective accompanies past tense, the resultant verbal suffix is necessarily that for present tense followed by “ta.”3 Determining verbal suffixes
The task we address here is to determine verbal
suffixes for a given input, a pair of an adnominal
usage of adjective in a certain context and a candidate verb given by our adjective-verb list.
From the viewpoint of language generation,
this task can be thought of as generating verbal
expressions where options are already given in
Table 1. A straightforward way for determining
verbal suffixes is to make use of lexical propertiesof verbs as constraints on generation. To manifest
them, in particular aspectual properties involvedin LCS, we first designed seven types of linguistic tests shown in Table 2. They are derived froma classical analysis of verb semantics in Japanese(Kageyama, 1996) and some ongoing projects onconstructing LCS dictionaries (Kato et al., 2005;Takeuchi et al., 2006). We then manually examined 128 verbs in Section 2 under those tests.
To determine the word sense in which the derivative relationship hold good, example sentences in
IPAL verb dictionary (IPA, 1987) for each verb
were used. For a verb which was out of the dictionary, we manually gave a sample sentence.
Since our aim is to explain why a certain verbal suffix is used for a given input, we have notfeverishly applied a machine learning algorithm tothe task. Instead, we have manually created a rulebased model shown in Table 3 using Cadc , where
each if-then rule assigns either of verbal suffixes inTable 1 to a given input based on verbal features inTable 2 and some other features below:• D: affix pair of the adjective and the candidate verb: e.g., “A shii-V mu” for “kuyashii(be regretful)” ⇔ “kuyamu (to regret)”• N : disjunction of semantic classes in a thesaurus (The Natural Institute for Japanese
Language, 2004) for the modified noun
• C: whether the adjective is head of clause

4 Experiment and discussion
By conducting an empirical experiment with Cadc
and Cado , we evaluate how our model (RULE)
properly determines verbal suffixes. A comparison with a simple baseline model (BL) is alsodone. BL selects the most frequently used suffix(in this experiment “ta”) for any given input.

Table 2: Linguistic tests for verbs derived from Lexical Conceptual Structure (Kageyama, 1996).
Label Description
Va whether the verb allows accusative case
Vb whether the verb can co-occur with a temporal adverb “ichi-jikan (for one hour)” or its variant
Vc whether the verb can co-occur with a temporal adverb “ichi-jikan-de (in one hour)” or its variant
Vd whether the verb can be followed by “tearu (perfective)” when its accusative case is moved to nominative
Ve interpretation of the verb followed by “tei-ru (progressive / perfective)”
Vf when followed by “ta,” whether the verb can have the perfective interpretation or just past tense
Vg whether the verb can co-occur with a sort of adverb which indicates intention of the action: e.g. “wazato (purposely)” and “iyaiya (reluctantly)”

Table 3: The rule-set for determining verbal suffixes, where “(non)” indicates non-paraphrasable.
Order Condition (conjunction of feature label =“value”)
Verbal suffixes
1 Va =“yes” ∧ Vb =“yes” ∧ Vf =“no” ∧re-ru
N =“except Human (1.10)” ∧D=“A ui–V bumu” ∨ “A i–V mu” ∨ “A asii–V u”
2 Va =“yes” ∧ Vb =“yes” ∧ Vf =“no” ∧ta
Vd =“no” ∧ N =“Mind: mind, attitude (1.303)”
3 Va =“no” ∧ Vg =“yes”
tata / tei-ru
4 Va =“no” ∧ Vf =“yes” ∧ D=“A i–V migakaru”
5 C=“clause” ∧ D=“A i–V maru”
ruta
6 Va =“no” ∧ Vf =“yes”ta
7 Va =“no” ∧ Vb =“yes” ∧ Vf =“no”
8 Vb =“yes” ∧ Vf =“no” ∧ Vc =“yes” ∧tei-ru
Vd =“yes” ∧ Ve =“progressive” ∧ N =“Subject (1.2)”
9 ∗(non)Table 4 shows the experimental results, whererecall and precision are calculated with regard
to input adjective-verb pairs. Among rules inTable 3, rules 1 (for “re-ru”), 3, 6, and 7 (for “ta”
where Va =“no”) performed much better than theother rules. This indicates that these rules and features in their conditions properly reflect our linguistic intuition. For instance, rule 6 reflects thata change-of-state intransitive verb expresses resultative meaning as adjectives when it modifies
Theme of the event via “ta” (Kageyama, 1996)
as shown in (1), and rule 2 does that a psychological verb modifies a nouns with “re-ru” when
the noun arouses the specific emotion, such as regretting mistakes (e.g., “kuyashii (be regretful)”⇔ “kuyama-re-ru (be regretted)”). The aspectual
property captured by the tests in Table 2 is used toclassify verbs into these semantic classes.
On the other hand, the rules for the other types
are immature due to lack of examples: we cannot
find out even necessary conditions to be “ru,” “teiru,” etc. What is required to induce proper conditions for these suffixes is a larger example collection and discovering another semantic property
and a set of linguistic tests for capturing it.

5 Conclusion and future work
In this paper, we focused on inter-categorial paraphrasing and reported on our study on an issue
in adjective-to-verb paraphrasing. Two generalpurpose resources and a task-specific rule-set havebeen handcrafted to generate proper verbal suffixes. Although the rule-based model has achieved
better performance than a simple baseline model,
there is a plenty of room for improvement.

Table 4: Recall and precision of determining verbal suffix for given adjective-verb pairs.
Verbal suffixesta (Va =“yes”)
ta (Va =“no”)
re-ru
ru
tei-ruta / tei-ru
No ruleTotal (RULE)
BL

Cadc
RecallPrecision
3/13
3/3
42/44
42/63
12/14
12/19
3/9
3/6
1/5
1/7
2/4
2/2
for 11 inputs
63/10063/100(63%)(63%)
57/10057/148
(57%)
(39%)

Cado
RecallPrecision
1/6
1/1
18/18
18/29
7/13
7/11
0/2
0/5
2/8
2/6
1/2
1/1
for 7 inputs
29/56
29/53
(52%)
(55%)
24/56
24/83
(43%)
(29%)

Future work includes (i) to enlarge our two
resources as in (Dorr, 1997; Habash and Dorr,
2003) evolving an effective construction method,(ii) intrinsic evaluation of those resources, and, ofcourse, (iii) to enhance the paraphrasing models
through further experiments with a larger test-set.

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