AUTOMATIC EXTRACTION OF TRANSLATION PATTERNS FROM
BILINGUAL LEGAL CORPUS
Makoto Ohara†, Shigeki Matsubara‡ and Yasuyoshi Inagaki§
Graduate School of Engineering, Nagoya University †Information Technology Center, Nagoya University ‡Faculty of Information Science and Technology, Aichi Prefectural University §Furo-cho, Chikusa-ku, Nagoya, 464-8603 Japan
makoto@inagaki.nuie.nagoya-u.ac.jp

ABSTRACTThe multilingualization of legal documents is desirable
for promoting the internationalization of the society.
Since it is vital to choose proper terms when translating legal documents, which include technical terms
and unique patterns, it is desirable to compile bilingual
dictionaries for each legal domain. Compiling basic
bilingual dictionaries for legal documents, however,is a difficult task because of the great range of legal
documents. This paper describes a method for automatically extracting translation patterns for legal document translation by using legal documents and theirtranslated documents. The proposed method extracts
translation patterns with Japanese bunsetsu-level units
from legal sentences and the translated sentences thatare properly aligned with each other. The proposed
method utilizes three indexes for pattern extraction:bilingual dictionaries, statistical co-occurrence information on the parallel corpus, and syntactic information based on dependency grammar. We have extracted
translation patterns from the Japanese Civil Code andits translation. The result has provided 80.5% precision and 49.1% recall, and the extracted translation
patterns will be useful for translating legal documents
and helping to construct a Japanese-English legal dictionary.
Keywords: translation pattern, legal document, bilingual corpus, dictionary, statistical alignment, dependency structure.

1. INTRODUCTION
In recent years, the multilingualization of Japanese legal documents, which include laws and judicial precedents, has been desirable to promote the internationalization of the society. Since we need the knowledge
not only of both languages, but also of legal operations, while translating legal documents, the task is noteasy for non-specialists. Besides, large quantities oflegal documents are expected to be translated. This
makes it desirable to improve multilingualization support systems, which include electronic dictionaries andmachine translation systems to reduce the cost of translation.
In order to accurately translate legal documentsby hand or to realize high-quality machine translation,
high-quality bilingual dictionaries are necessary. Although large-scale electronic dictionaries have been
compiled in recent years [5], they are not necessarily adequate due to the necessity for domain-specificbilingual dictionaries for legal documents.
On the other hand, in recent years, several methods
for automatic extraction of translation patterns frombilingual corpora have been proposed as developing
NLP technologies (for example, [1, 6, 8]). Maximizing
their benefits by taking into account the characteristicsof legal documents will enable automatic extraction of
the translation patterns for legal documents, along witha reduced development cost. Such extracted translation patterns are useful for both machine translation
and bilingual dictionary compilation.
This paper describes a method for automatic extraction of translation patterns from parallel legal corpora. The proposed method extracts the translation
patterns by estimating bunsetsu-level phrase alignment1 based on three indexes: existing bilingual dictionaries, stochastic correspondence information anddependency structures.
We have carried out an experiment on JapaneseEnglish translation pattern extraction from theJapanese Civil Code, which is one of the most representative Japanese laws. The result has provided
1

A bunsetsu is one of the linguistic units in Japanese,and roughly corresponds to a basic phrase in English. A
bunsetsu consists of one independent word and more thanzero ancillary words.

80.5% precision and 49.1% recall on the evaluation
with 100 sentences in the Code, suggesting the possibility of support for translating legal documents andcompiling a bilingual legal-domain dictionary.
The remainder of this paper is organized as follows: Section 2 explains characteristics of legal documents and the task of translating them. Section 3 describes the indexes for automatic extraction of translation patterns, and Section 4 gives our extraction
method. Section 5 reports on the experiment on pattern extraction and provides the results.

2. LEGAL DOCUMENTS AND THE TASKOF TRANSLATING THEM
In this section, we firstly describe the characteristicsof legal documents and the issues involved with theirtranslation task. Next, we explain the necessity of abilingual dictionary for legal translation task and automatic extraction of legal translation patterns.
2.1. Legal document translation
Translating Japanese legal documents is an importanttask for increasing international understanding of society. Some problems have been experienced with translation of legal documents to date, including unevenness of the translation quality and the limited use of
the translated documents in organizations. In recent
years, this fact has made it necessary for some parties
to share good-quality translations [3]. The unificationof translation patterns is essential for sharing the largescale translated documents2 . To achieve it, we needbilingual dictionaries whose entries include words that
relate to basic laws. It takes a lot of costs to compile
a dictionary, so automatic extraction of translation patterns is required to reduce its cost.
2.2. Linguistic characteristics of legal documents
We have investigated the characteristics of legal documents using the Japanese Civil Code, which is one of
the most representative and basic laws. For the analysis, we used 2,129 sentences in this Code3 . Table 1
shows the average length of sentences, the number ofdifferent words/bi-grams, and the perplexity for morphemes. The same types of data of 2,129 sentencesin Mainichi newspapers(in 1995), which is one of themost famous newspapers in Japan, are shown in the table for comparison. The length of a sentence is defined2

Since the selection of words in law is made very carefully, it
is also required to carefully choose the translation patterns [9].
3The Japanese Civil Code is composed of five Books: General
Provisions, Real Rights, Obligations, Relatives, and Succession.
Though Books I-III are described in literary style in the original,
we used documents that are converted to colloquial style.

Table 1: Comparison between legal documents andnewspapers
item
average sentence length
No. of different morphemes
No. of different bi-grams
word perplexity

Civil Code
30.0
2000
10460
159.2

newspapers
22.5
7197
26951
575.6

as the number of morphemes in it. The basic form
is adopted for the number of different morphemes/bigrams.
The sentences in legal documents are generally
longer than those in newspapers. On the other hand,the number of different morphemes is only 27.8% thatin the newspapers. This supposes legal documents areorganized by some constant variation of vocabulary,different from that of newspapers that include many
proper nouns, and it also supposes that it is useful tocompile a bilingual dictionary of words that frequently
occur in legal documents. This expectation is encouraged by the fact that the words used for legal documents are settled in meaning and usage [9]. Moreover,the fact that the perplexity in the legal documents issmaller than that in the newspapers is also encouragedby the existence of some characteristic patterns.

3. INDEXES FOR TRANSLATIONPATTERN EXTRACTION
There exist many previous researches for translation
pattern extraction. In this section, we describe threeindexes used in the previous researches for automatic
extraction of translation patterns, based on the linguistic characteristics of legal documents: existing bilingual dictionaries, statistical information for calculating the measurement of whether a particular word-pairis aligned, and the dependency structure for use in extraction.
3.1. Existing dictionaries
Some large-scale bilingual dictionaries have been consolidated and published in recent years [5]. Bilingual
dictionaries for legal documents have also been constructed and marketed, although they are small-scaleand do not cover all legal terms (for example, [11]).
When extracting translation patterns from bilingual
corpora, word alignment can be inferred by looking up
bilingual dictionaries. The proposed method in this paper aims for high precision and recall of the extracted
patterns in the corpus by utilizing existing bilingual
dictionaries, as seen in some preceding works(for example, [1]).

3.2. Statistic information of corpus
Preceding researches for extraction of translation patterns from parallel corpora often employ methods forstatistical inference of the alignment by occurrences ofeach word in the corpus (for example, [6, 8]). The statistical methods can accurately extract translation patterns that frequently occur in the corpus. Therefore
the proposed method may infer the translation patterns
that frequently occur in the legal documents but do
not exist in bilingual dictionaries. Thus the proposed
method infers translation patterns by statistical information as well as by information from existing dictionaries.
In translated legal documents, few free translations and omissions can exist, since it is required thatthe information on the source language is represented
equally for the properties of a legal translation as we
saw in section 2. Besides, the fact that the number ofdifferent words is small leads us to expect that words
in legal documents occur more frequently than those innewspapers. Thus, in the translated legal documents,
most of which are small, statistical information would
be useful for inference of word alignment, although
the corpora which include over 10,000 sentences havebeen used for the stochastic word alignment methods.
3.3. Syntactic information based on dependency
structure
The proposed method utilizes dependency structure inboth source and target sentences as the syntactic information for extraction, just like existing dictionaries
and statistical information. Both Japanese and English,
each of which belongs to a different language family,
have similar dependency structures that mean the modification relation between two phrases, thus can be utilized for extraction.
An example is shown in Fig. 1, which consistsof the structures of a Japanese legal sentence “Konobaai-ni-wa, zenjyou-no kitei-ni-yotte tokubetsu-dairinin-wo sennin-shinakereba-naranai,” which is translated as “In such cases, a special representative shallbe appointed in accordance with the provisions of
the preceding Article.” In the figure, for instance,
the bunsetsu-level translation patterns “tokubetsudairi-nin-wo”-“a special representative” and “senninshinakereba-naranai”- “shall be appointed,” exist. Examining each sentence carefully reveals that theJapanese bunsetsu “tokubetsu-dairi-nin-wo” depends
on “sennin-shinakereba-naranai,” and English phrase“a special representative” also depends on “shall be appointed.” Moreover, for the translation patterns “kiteini-yotte”-“in accordance with the provisions”4 and               ! "#$&%'    (  !) *   %   *+%,-*./ 0213) 14  
 65  1 7$8#%  59%    #:-&; ')*$88(  <5(5=&#  "& 1>1>%?"#-*1  ) @)  59%,; ABC
9DE@)  59%  1  "FG H %6/B1>8 I

Figure 1: Similarity of dependency structures inJapanese and English“sennin-shinakereba-naranai”- “shall be appointed,”
the same is worked out on dependency structure.
Parsing tools with relatively high accuracies enabled by the development of recent statistical parsing
techniques have been developed and released [2, 14].
Some previous researches apply these techniques for
extracting translation patterns(for example, [1]). The
proposed method also utilizes them, and we expect thatthe tools can parse with reasonable accuracy becauseof the linguistically faithful structures in legal documents, although legal sentences are generally long andcontain particular legal expressions [10].

4. METHOD FOR TRANSLATIONPATTERN EXTRACTION
In this section, we propose a method for extracting
translation patterns using parallel legal corpora, considering the three indexes described in Section 3.
4.1. Outline of the method
Fig. 2 shows a flow chart of the proposed method,which extracts Japanese bunsetsu-level translation patterns from a Japanese legal document and its translation in which sentences are properly aligned. The process is as follows:
Step 1: Parse both legal sentences in bunsetsus by utilizing dependency parsing tools.
Step 2: Infer word alignment according to dictionaries and Dice coefficient of each word-pair. If thealignment has ambiguities, disambiguate them.
Step 3: Infer bunsetsu-level alignment based on thedisambiguated word alignment.
Step 4: Infer the bunsetsu alignment that is not acquired by the word alignment, based on the dependency structure of both languages and the acquired bunsetsu alignment.

4

The sequence “in accordance with the provisions” consists of
two chunks based on the definition in this paper. We take it as one

chunk for simplification in Fig. 1.

      "!#$%'&() *+, 

4.3.2. Inference of word alignment by statistical information-. /,,01(23$

There exist many and sophisticated ways to infer wordalignment in bilingual corpus(for example, [4, 12]).
For simplification of inference, the extraction method
we adopted in this paper treats the Dice coefficient asstatistical information, which is one of the simplest
ways for deciding whether the word-pairs are aligned.
It stands for the ratio of co-occurrences of word-pairs
and independent occurrences of each word in the corpus, and is based on the assumption that the higher
this value, the higher the possibility that the word-pair
has aligned. The Dice coefficient Dice(wj , we ) can becalculated as follows:

45. 6 7+(  897;:"8<(  8<(=(>8 9  ? @88<A,BCED : 8<($FB )G8H (JI?   D )3CK/ L 8 7M7+G GNI? OP Q@RD,)SC1:"8<(T8H(>(= 8<,, IU D==D W X$  &DA^ 8<( W X$ , 8<(M(>8< 9  
Z. 6,7M(, L87QD*[+>D  8H(>(=8<,, ? C,()3$\QD+'D W X$  8<(=(>,8<,, JI?   )3$\G&,L 8<(=(>8H   Y]I/  0 >(=D,%D(U D==D W X$   8H(>(=8<,,_Y

`5. a )8< 87 (>D,bQ 8H   0
U D*V>D W X$   8<(=(Y>8<,,Figure 2: Configuration of the proposed method
We describe these steps in detail in the remainderof this section.
4.2. Dependency parsing with bunsetsu level
The proposed method acquires bunsetsu-level dependency structures using parsing tools. Since the translation pattern extraction is based on the Japanesebunsetsu-level chunks, Japanese sentences are segmented into bunsetsus. English sentences are also
adjusted with bunsetsu-like chunks using some rules
based on parts-of-speech and base NP, as well as dependency analysis.
4.3. Inference of word correspondences
4.3.1. Inference of word alignment by dictionaries
The proposed method infers word-level alignment, before the inference of bunsetsu-level alignment. At
first, the word correspondences inferred as words fortranslation are generated by looking up the relevant
words in bilingual dictionaries. Both the surface andbasic forms are adopted for the search, and word sequences bounded to five morphemes/words are also
looked up. The word correspondence for a sequence
is dealt with as one correspondence. A Japanese collocation “houtei dairi nin,” for example, is composedof three morphemes “houtei,” “dairi” and “nin,” so“houtei dairi,” “dairi nin” and “houtei dairi nin” arealso targets to look up in addition to each morpheme.
Function words are not targets to look up; the ambiguities of word correspondences are increased because the same function words generally exist in thesame sentence.

Dice(wj , we ) =2 · fje,(fj + fe )(1)

where fj is the frequency of the Japanese word wj , andfe is the frequency of the English word we , and fje isthe frequency of wj and we that occur on the aligned
sentences.
The proposed method acquires word correspondences by inferring that word-pairs with a Dice coefficient above a fixed threshold are aligned.
4.3.3. Disambiguation of word correspondences
Word correspondences might have ambiguities in situations where the same word exists more than oncein a sentence or some word-pairs for one word havehigher Dice coefficient than a fixed threshold. The
disambiguation algorithm in the proposed method isbased on Aramaki’s method [1]. This method supposesthat some correspondences exist in the neighborhoodof a correspondence where word correspondences are
given in the aligned sentences.
The proposed method disambiguates word correspondences using the number of correspondences in
the neighborhood of the ambiguous correspondence,
based on the above assumption. We define the neighborhood as the following and preceding two bunsetsus of the indicated bunsetsu. The disambiguation
method selects the word correspondence that has themost word correspondences in the neighborhood andrejects the others. Fig. 3 shows an example of an
ambiguity in two correspondences, “shadan”-“Bodies”
and “houjin”-“Bodies”. The number of word correspondences in the neighborhood of “shadan”-“Bodies”
is one; the only correspondence “mokuteki”-“object,”
exists as shown in Fig. 3(a). On the other hand,the one in the neighborhood of “houjin”-“Bodies”
is zero; there are no word correspondences in theneighborhood, as shown in Fig. 3(b). Then, theword correspondence “shadan”-“Bodies” is selected
and “houjin”-“Bodies” is rejected.

  

EGFIH JLKNM OPHAQSRUT V RWQYX Z[RWQQH@\]^RWJ_XNHAJIZH`\a J b,FIH JIH a>c FO^RWQdF_ReReX RUTfbgFhH Z!RWQdQH`\]^RWJ_XNH<JIZH i \jFIk!XNkJ l m i n ReX a H@\ l oqp
 / :   ( ;<-; : - : =<>  
 !   % :  ?
   !"#$%  : &  " )@ : #,,A/BC : %
 '&  % ) 2;% : %!
   &   ?
( ) *%!,+*  -
%  ./  %!   %!-0%1
;!/ : +2#3#/*4% : $ : ,AD : ;!- : &  56    (  -75%
;!> 9
  . ( ( / 8  ( +  %! 9(a)
EGFIH JLKNM OPHAQSRUT V RWQYX Z[RWQQH@\]^RWJ_XNHAJIZH`\a J b,FIH JIH a>c FO^RWQdF_ReReX RUTfbgFhH Z!RWQdQH`\]^RWJ_XNH<JIZH i F_R'Kj a J k l i m ReX a H@\ k npo / :   ( ;<-; : - : =<>  
 !   % :  ?
   !"#$%  : &  " )@ : #,,A/BC : %
 '&  % ) 2;% : %!
   &  ( ) *%!,+*  -
%  ./  %!   %!-0%1
;!/ : +2#3#/*4% : $ : ,AD : ;!- : &  56    (  -75%
;!> 9
  . ( ( / 8  ( +  %! 9(b)     !"#$%   

'& (       !"#$% '& (    )   *+, !"#- 

.&     /     *+, !"#- 

0&  Figure 4: Generation of candidates based on word correspondence“quarter,” “san”-“three” and “ijyou”-“more” are inferred. In such a case, “of three quarters or more” isa candidate of correspondence for “yon-bun-no-sanijyou-no” as well as “of three quarters” and “or more”.
4.4.2. Selection of candidates
Bunsetsu-level correspondences are selected by a measure of the ratio of content words included in word correspondences. The measurement of equivalency E isdefined as follows:
E=

C(included word), (2)C(Jcontentword) + C(Econtentword)

Figure 3: Disambiguation of word correspondences
4.4. Inference of bunsetsu correspondences
Bunsetsu correspondences are inferred based on theword correspondences inferred by dictionaries and statistical information. The inference includes two steps:(1) generation of translation pattern candidates, (2) selection of the candidates.
4.4.1. Generation of translation pattern candidates
Bunsetsu-level correspondence candidates are generated based on the inferred word correspondences.
Japanese bunsetsus and English bunsetsu-level
phrases that include word correspondences are inferred as candidates for bunsetsu-level correspondence. When a bunsetsu has some word correspondences in more than one bunsetsu in the other language, they are also generated as candidates. Fig. 4
shows an example for generating candidates. In the
figure, for a Japanese bunsetsu “yon-bun-no-san-ijyouno” and two English phrases “of three quarters” and“or more,” three word correspondences “yon-bun-no”-

where C(included word) is the number of words included in the word alignment, C(Jcontentword) is
the number of Japanese content words in the candidates, and C(Econtentword) is the number of English content words. The method selects the candidatethat has the highest measurement, and rejects the othercandidates including the selected bunsetsu.
Fig. 5 shows correspondence selection in somecandidates. The candidate that has the maximum E
is the correspondence “yon-bun no san ijyou no”-“ofthree quarters or more,” which has three word correspondences, thus E = (3 + 3)/(3 + 3) = 1, and thiscorrespondence is selected, whereas the others are rejected.
4.5. Correspondence inference by syntactic information
Some bunsetsus that have not yet been inferred ascorrespondences are extracted as translation patterns,
using dependency structure and bunsetsu correspondences inferred by word correspondences. The proposed method uses five heuristic rules for legal sentences to extract new correspondences or combines

yonbun-no-san-ijyou-noof three quarters

yonbun-no-san-ijyou-no

or moreof three quarters

E = 4/5or moreE = 2/4

Rule 3: When dependents of each candidate bunsetsu
are inferred as correspondences, and the otherbunsetsus that depend on the same bunsetsu as
the candidate have already been inferred as correspondences (Fig. 6(c)).

yonbun-no-san-ijyou-noof three quarters

or moreE = 6/6

Figure 5: Measurement of equivalency                    # $&% ' (*),+ -

!"

!"
# 23% ' (*),+ 4

5.1. Conditions



Rule 5: When the root bunsetsu in a sentence and a
non-root bunsetsu in another are inferred as acorrespondence, and the non-head bunsetsu andthe root bunsetsu in the other have a dependency
relation, combine the non-root with root bunsetsu (Fig. 6(e)).

5. EXPERIMENTS# ./% ' (*)0+ 1

  

Rule 4: When the dependent of the candidate and abunsetsu that depends on the candidate are inferred as correspondences, and these correspondences have a dependency relation, combine thecandidate with either correspondence. The selection is based on the two measurements ofequivalency, as shown in section 4.6.2. (Fig.
6(d)).



 





Figure 6: Rules
bunsetsus with existing correspondences. The proposed method aims to extract translation patterns withhigh precision, using both syntactic information andthe already-extracted correspondences.
Rules 1, 2 and 3 extract new correspondences between bunsetsus, whereas rules 4 and 5 combine bunsetsu with existing correspondences. Details of therules and illustrated figures are as follows:
Rule 1: When dependents of each candidate bunsetsu
are inferred as correspondences, and at least one
correspondence that depends on each candidate
bunsetsu is inferred (Fig. 6(a)).
Rule 2: When both candidate bunsetsus are the rootsof sentences and at least one correspondence
that depends on each candidate bunsetsu is inferred (Fig. 6(b)).

We performed translation pattern extraction experiments using the Civil Code, which is one of themost representative laws in the system of Japaneselaw. In these experiments, we utilized 1994 aligned
Japanese/English sentences that can be prepared andtranslated, for the Dice coefficient. The threshold forthe Dice coefficient was decided according to a preexperiment and set as 0.6, a score that can maintainthe precision of the extracted patterns.
The dictionaries we used for word correspondences were EDR bilingual dictionaries [5], whichfeatures approximately 360,000 entries for JapaneseEnglish and about 290,000 entries for EnglishJapanese, and a bilingual dictionary for business andlegal terms by Inter Press [11], which contains about
30,000 entries for both Japanese-English and EnglishJapanese.
For the Japanese dependency analysis, we used aJapanese morphological analyzer ChaSen [15], a multipurpose chunker Yamcha [13] and a Japanese dependency analyzer Cabocha [14]. Cabocha has a 91% dependency accuracy for newspapers.
For English, after chunking with Japanesebunsetsu-like chunks, we calculate dependency relation between the chunks. These processes have been
performed based on the phrase structures of sentences
generated by Charniak’s parser [2]. Charniak’s parser
has 90.1% of label precision/recall. The proposed
method calculates English dependency structures fromphrase structures by determining the head child in eachTable 2: Experimental results
item
proposed method
dictionaries+syntax
dictionaries+statistics
dictionaries
statistics

precision (%)
80.5(43.5)
79.5(43.4)
80.7(42.0)
80.2(43.1)
83.5(52.2)

Table 3: Examples of extracted patterns
recall (%)
49.1(26.5)
48.0(26.1)
46.3(23.7)
44.9(23.7)
16.6(10.2)

Japanese legal term
koukyou-no (fukushi-ni)
kono houritsu-wa
kaisyaku-shi-nakereba-naranai
hani-nai-de

Its translation
to the public welfare
this Codeshall be construed
within the scope

Table 4: Extraction by each dictionary
CFG rewriting rule [7]. For chunking English sentences, we adopt Yamamoto’s method[6].
5.2. Results
We evaluated the precision and recall of extracted
translation patterns with 100 sentences, which weremanually aligned by bunsetsu-level. Table 2 showsthe experimental results. In this paper, we definethat a correct answer is the one that completely or
partly matches with one correspondence that is manually aligned. This definition takes into consideration of manual modification. Parenthesized numbersin the table are the recall and precision based on thecomplete matches. Precision is defined as the ratio of
extracted translation patterns that are included in thecorrect correspondences, while recall is defined as theratio of the correct correspondence that matches theextracted translation patterns. The words “dictionaries+syntax/statistics” mean, in this result, that the results of the experiment using existing dictionaries andsyntactic/statistical information, but not using statistical/syntactic information, respectively. The word “dictionaries” means that extraction has been done only
via looking up dictionaries, and the word “statistics”
means that looking up was performed only with theDice coefficient.
The precision of the proposed method is better thanthose of other related researches, and the recall forhigher than that obtained by other statistical extraction
methods [1, 6, 8]. Moreover, the use of statistical andsyntactic information caused a 4.2% increase of recall
for dictionaries only, retaining its high precision.
In the case that translation patterns are extractedwith only statistical information, inference of correspondences can be done with relatively high precision,
although its recall is lower. This result suggests thatthe Dice coefficient has a beneficial effort regardlessof the small corpus and its simple inference method.
Besides, the statistical information gives an increaseof 1.4% in recall in comparison with extraction with
only dictionaries.
Using the five rules based on dependency structure,a rise of 2.8% recall has been achieved with similar
high precision, in the case that bunsetsu-level align-

item
both
EDR[5]
Inter Press[11]precision (%)
80.2
78.2
81.2

recall (%)
44.9
38.8
36.7

ment is given with about 80% precision and 45% recall.
Examples of extracted patterns are shown in Table 3. The proposed method can acquire the patterns
that have several bunsetsus/basic phrases, as shown an
example “kono houritsu-wa” and “this Code,” which
has two bunsetsus in the Japanese pattern. Besides,
relatively longer translation patterns are acquired although each words exist in dictionaries, as shown in
the example “hani-nai-de” and “within the scope.” In
this pattern, “han’i-nai-de”-“within the scope” doesn’t
exist in the dictionaries, although “han’i”-“scope” and“nai”-“within” exist as translation words in EDR dictionary5 .
5.3. Relation between two dictionaries
To investigate each dictionary’s capabilities, we extracted translation patterns using only one dictionary.
Table 4 shows the results.
Each of dictionary has almost the same contribution in this extraction, although EDR dictionaries havehigher recall and Inter Press dictionary has higher precision.
5.4. Evaluation for disambiguation algorithm
To evaluate the disambiguation algorithm of word correspondences adopted by the proposed method, we
compared this method with random choice in selecting
ambiguous alignment. Figure 5 shows the precisionand recall, where they use the Dice coefficient and thedictionaries, disambiguation algorithm in each condition.
As shown in the table, 3.3% higher precision and
0.5% higher recall is achieved by utilizing disambigua5
The proposed method infers a word correspondence “nai”“within” based on looking up “nai,” although doesn’t looking up“within” because it is a function word.

Table 5: Evaluation of disambiguation method
itemneighborhood:1
neighborhood:2
neighborhood:3
random choice

precision(%)
80.4
80.7
79.9
77.4

recall(%)
46.8
46.3
46.1
45.8

tion algorithm.

6. CONCLUSION
In this paper, we have described a method for automatic extraction of translation patterns from parallel legal corpora. For the indexes of extraction, we
utilized bilingual dictionaries, the Dice coefficient ina corpus, and syntactic information based on dependency structure, concerning the characteristics of legal documents and their translation. Using existingbilingual dictionaries and parsing tools, we extracted
translation patterns with high recall, while maintaining high precision. By manually modifying the acquired patterns, they become useful for legal translation tasks. The proposed method has utilized the Dicecoefficient and the rules applied rather infrequently
for dependency structures, thus future work will makemore practical use of statistical and syntactic information. We also plan to compile a dictionary for legaltranslation.

7. ACKNOWLEDGEMENT
The authors are grateful to the reviewers of this paper
for their helpful comments. The authors would liketo thank Prof. Yoshiharu Matsuura, Associate Prof.
Frank Bennett and Associate Prof. Tokuyasu Kakuta
for their beneficial instructions and discussions on legal informatics, and also thank Associate Prof. Katsuhiko Toyama and Dr. Yasuhiro Ogawa for their valuable advice. This research is partially aided by the
Grant-in-Aid for Young Scientists of the Ministry of
Education, Science, Sports, and Culture, Japan and the
Nakajima Foundation.

References[1] E. Aramaki, S. Kurohashi, S. Sato, and H. Watanabe, “Finding Translation Correspondences from
Parallel Parsed Corpus for Example-based Translation,” Proc. of MT Summit-2001, pp. 27-32,
2001.
[2] E. Charniak, “A Maximum-Entropy-Inspired
Parser,” Technical Report CS-99-12, Brown University, 1999.

[3] F. G. Bennett, “Distributed Legal Translation Project,”
http://www.nomolog.nagoyau.ac.jp/˜bennett/[4] I. Dan Melamed, “Models of Translational
Equivalence among Words,” Computational Linguistics, Vol. 26, No. 2, pp. 221-249, 2000.
[5] Japan Electronic Dictionary Research Institute,Ltd., “EDR Electronic Dictionary Technical
Guide,” http://www.jsa.co.jp/EDR/[6] K. Yamamoto, and Y. Matsumoto, “Acquisitionof Phrase-level Bilingual Correspondence using
Dependency Structure,” Proc. of COLING-2000,
pp. 933-939, 2000.
[7] M. J. Collins, “A New Statistical Parser Based on
Bigram Lexical Dependencies,” Proc. of ACL-96,
pp. 184-191, 1996.
[8] M. Kitamura, and Y. Matsumoto, “Automatic Extraction of Word Sequence Correspondences in
Parallel Corpora,” Proc. of WVLC-96, pp. 79-87,
1996.
[9] M. Maeda, “Housei-Shitsumu,” Gyousei, 1983
(in Japanese).
[10] M. Ohara, S. Matsubara, and Y. Inagaki, “Legal
Sentence Parsing Based on Syntactic Patterns,”
Proc. of IEICE General Conference, 2003 (InJapanese).
[11] Nichigai Associates. Inc., “INTER PRESS
Japanese/English legal dictionary,” 2000.
[12] P. F. Brown, S. A. Della Pietra, V. J. Della Pietra
and R. L. Mercer, “The Mathematics of MachineTranslation: Parameter Estimation,” Computational Linguistics, Vol. 19, No. 2, pp.263-311,
1993.
[13] T. Kudo, and Y. Matsumoto, “Chunking with
Support Vector Machines,” Proc. of NAACL2001, pp. 192-199, 2001.
[14] T. Kudo, and Y. Matsumoto, “Japanese Dependency Analysis using Cascaded Chunking,”
Proc. of CoNLL-2002, pp. 63-69, 2002.
[15] Y. Matsumoto, et al., “Morphological Analysis System ChaSen version 2.2.1 Manual,”
http://chasen.aist-nara.ac.jp/