Extracting French-Japanese Word Pairs from Bilingual Corpora based onTransliteration Rulesy

Keita Tsuji , Beatrice Daille , Kyo Kageura

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japanfkeita, kyog@nii.ac.jp
University of Nantes
IRIN, 2, rue de la Houssinire BP 92208, 44322 Nantes cedex 3, France
daille@irin.univ-nantes.fr
AbstractyIt has been shown so far that using transliteration rules to extract Japanese Katakana and English word pairs is highly
useful and promising. But for Japanese-French pairs, the method is not guaranteed to work, because only a very few
Japanese Katakana words are borrowed directly from French. In this paper we will show the possibility of extracting
Japanese Katakana and French word pairs based on transliteration from loosely aligned Japanese French bilingual corpora.
The method applies all the existing transliteration rules to each mora unit in a Katakana word, and extracts the French
word which matches or partially-matches one of these transliteration candidates as translation. For instance, if we have
`グラフ' in the Japanese part of a bilingual corpora, we generate such transliteration candidates as <graf>, <graphe>,<gulerph>,... and identify similar words from French part of the corpora. The method performed reasonably well,achieving 80% precision at 20% recall. We had also observed that Japanese-English transliteration rules worked well for
extracting Katakana-French word pairs.
1.

Introduction

In this paper we intend to discuss the possibility of
extracting Japanese Katakana and French word pairs
from loosely aligned Japanese French bilingual corpora. It was shown that using transliteration rules
to extract Japanese Katakana and English word pairs
is highly useful and promising, as it can extract low
frequency pairs with high precision (because mostJapanese Katakana words originated from English)
and even contributes to the improvement of the statistical methods.
For Japanese-French pairs, the method is not guaranteed to work, because only a very few Japanese
Katakana words are borrowed directly from French.
Nevertheless, this is worth pursuing for the following
reasons. (a) Using transliteration rules is expected towork at least to some extent, due to the similarity between English and French words. (b) In addition, if,for instance, proper names are tagged in French, theextraction of Japanese Katakana equivalent will work
even better, due to the constancy of the spellings ofproper names. (c) Given the situation that there is a
dearth of Japanese-French parallel or comparable corpora, all the doors should be knocked to promote theimprovement of the availability of parallel or comparable corpora between non-English language pairs. If
the result of Japanese Katakana and French word pairextraction is reasonable, especially in terms of precision, we can use them as anchoring points for matchingcorresponding articles, thus enriching the parallel resources.

2.

MethodOur method is basically similar to (Tsuji, 2001),
which is invented for Japanese Katakana-English wordpair extraction. The method rst construct transliteration rules manually and then extract translational
Katakana-French (or English) word pairs from bilingual corpora based on those rules. The di erence between our method and that of (Tsuji, 2001) is thatwhile he used Hepburn (Hebon) transliteration rules
as supplement, we did not use it.
2.1. Construction of Transliteration Rule
The procedure is as follows (henceforth we represent the obtained rule as 'TR'):(1) Decompose Katakana word in some source list intomora units.
(2) Based on the French counterpart word, extract
transliteration rule for each unit manually.
(3) Repeat (1) and (2) for all the word pairs in thelist and rank the rules based on their frequency.
2.2. Extraction of Traslational Word Pairs
First we de ne the following symbols and functions:

J : Katakana word
F : French word
L(w): The number of characters of word w
T (J ): The transliteration candidate of word JS (w1; w2): The longest common subsequence of
w1 and w2Dice(k; m; n) = k  2=(m + n)

Extracting Katakana-French translations frombilingual corpora is achieved as follows:(1) Pick up J and decompose it into units according to
the same framework we used at TR construction.
(2) Using all the transliteration rules in TR, generate
all the possible transliteration candidates for J .
Henceforth we represent the i-th transliteration
candidate of J as Ti(J ).
(3) Pick up F which co-occurred with J and identify
the longest common subsequence with each Ti(J ).
(4) If the following P (J; F ) exceeds certain threshold,extract pair J and F as translation: P (J; F ) =max Dice(L(S (Ti(J ); F )); L(Ti(J )); L(F )).
i

In the case that J is ' グラフ' and the transliteration rule is as in Table 1, transliteration candidates
such as <graf>, <graphe>, <gra >, ..., <guler >and <gulerfe> are obtained. Therefore, P(グ ラフ,graphe) becomes as follows:P(グラフ, graphe)= max(Dice(L(S(graf,graphe)),L(graf),L(graphe)),Dice(L(S(graphe,graphe)),L(graph),L(graphe)),Dice(L(S(gra ,graphe)),L(gra ),L(graphe)),...
Dice(L(S(guler ,graphe)),L(guler ),L(graphe)),Dice(L(S(gulerfe,graphe)),L(gulerfe),L(graphe)))= max( 0.60, 1.00, 0.55, ..., 0.31, 0.31 )= 1.00
It indicates that ' グラフ' and 'graphe' are likely to
be translation.
グggue
gu

ラra
lal
lu
rler

フfphe
fe

Table 1: Transliteration rule for `グ ', `ラ' and `フ'
Our basic idea is as follows. If we use all thetransliteration rules and generate all the possible
transliteration candidates, the `correct' transliterationis always in that set, though we do not know whichis the correct one. We use bilingual corpora to resolve this, assuming that there exists a transliterationof Katakana word in French part of bilingual corpora.
We regard the transliteration candidates which actually exist as words in the French part as the correct
ones. However, the transliteration rules we can obtain
are always insucient. Therefore we do not use exact
matching. Instead, we use Dice and extract the actual
word in the French part of corpora, not T (J ), whose
maximum of Dice is high, as translation.

2.3. Device for Time-saving
The previously-mentioned procedure requires muchcomputational time when applied to actual data, because (1) using all rules in TR often leads to the combinatory explosion of the number of transliteration
candidates, (2) identifying the longest common subsequence often requires much time.
As for (1), we decided to apply less transliteration rules to longer Katakana words. At TR construction, we have ranked transliteration rules according to their frequencies. We applied the top12=(the number of units in J ) + 1 rules to each unitof J . 1 In the case that TR is as in Table 1 and Jis `グラフ', the number of rules applied to each unit is12=3 + 1 = 5. Therefore 3  5  4 transliteration candidates are examined instead of 3  6  4 candidates.
As for (2), we used `NPT score' in (Collier et al.,1997) for computing L(S (T (J ); E )). It is an abbreviated version for identifying the longest common subsequences.
3.

DataWe will explain the bilingual corpus we used for extraction experiment and the source list for constructing
transliteration rules.
3.1. Bilingual Corpus
We extracted French-Japanese translations frombilingual corpus of news articles (Le monde diplomatique: 21 articles). The Japanese and French parts ofthese corpora are processed by morphological analyzer
ChaSen2.0b and Brill part-of-speech tagger respectively. We regarded one article as one segment fromwhich we extract translation pairs. Our extraction target was the translational Katakana-French single-wordnoun pairs in each segment of corpus. The number ofthese Katakana-French translation pairs was 1,202.
The basic quantities of the corpora are shown in
Table 2. In Table 2, `Token' indicates the number oftokens of ChaSen morphemes in the Japanese part ofcorpus and the number of French words in the Frenchpart of corpus respectively. `Type' indicates the number of types of these morphemes or words. `SWN' indicates the number of types of single word nouns and`SWNK' indicates the number of types of Katakana
single word nouns.
Token Type SWN KSWN
Japanese 82,062 8,501 4,904 1,098
French60,855 9,751 4,356{Table 2: Basic Quantities of Bilingual Corpus
1
If 12=(the number of units in J ) + 1 is not an integer,
we adopted the maximum integer which does not exceed
it. In the case that the number of units in J is 5, we used
3 rules for each unit.

3.2. Source List for TR
We used two kinds of transliteration rules, one ofwhich was extracted from Katakana-French word pairs
and the other of which was extracted from KatakanaEnglish word pairs. The reason we used the latter isthat Katakana-English word pairs are easy to obtainand we assume that there is only small di erence between French and English words.
We extracted 1,000 Katakana-French andKatakana-English word pairs from ConcordeJapanese-French Dictionary and EDICT respectively. The basic quantities are shown in theTable 3. In Table 3, `Tot' indicates the total numberof Katakana-French and Katakana-English wordpairs. `Crr' indicates the pairs whose phonetic units
completely correspond and, therefore, from which
we can extract the transliteration rules. Note that
90% of Katakana-English word pairs belong to thistype and more than half of Katakana-French wordpairs belong to this type. `Abb' indicates the numberof the pairs one of which is the abbreviation of thelatter. For instance, (アパート , appartement) and (ガム, chewing-gum) belong to this type. `Inv' indicatesthe number of the pairs whose order of constituent
unit is di erent. For instance, (サテライト スタジオ, studio-satellite) and (アルペン スキー, ski alpin)
belong to this type. Katakana-French pairs contains
this type of pairs more than Katakana-English pairs
do, but the number of pairs was not so large as weexpected. `Rlt' indicates the pairs which do not fall onto the above types but part of which correspond. And`Noc' indicates the pairs which have no corresponding
part. Note that about 63% of Katakana-French pairs
have some correspondences from which we can extract
transliteration rules.
We used 512 Katakana-French pairs in `Crr' assource for TR construction. Besides, we randomly
selected 512 Katakana-English pairs from 900 pairsin `Crr' and used them for the other TR construction. The former TR is the Japanese-French TR andthe latter is the Japanese-English TR. We extracted
Katakana-French pairs from bilingual corpus based on
the former TR and the latter TR.
Tot Crr Abb Inv Rlt Noc
J-French 1,000 512 32 13 76 368
J-English 1,000 900 37
19 53
Table 3: Basic Quantities of Katakana-French andKatakana-English Pairs

1996), (Collier et al., 1997) and (Tsuji, 2001). But(Ishimoto and Nagao, 1994) did not go into detailsof transliteration method. (Matsuo and Shirai, 1996)
used only consonants for Japanese English matching.
The vowels which we think have useful information
were ignored. (Kumano, 1995) extracted pairs whose
estimated pronunciations exactly matched. But themethod to estimate the pronunciation of English word(which seems dicult) was not clearly explained. On
the other hand, (Collier et al., 1997) did not discard
vowels and showed their procedure clearly. (Tsuji,2001) took up parts of the method of (Collier et al.,1997) and compared the e ectiveness with his own
method. And his method was found more e ective
than that of (Collier et al., 1997). Therefore weadopted the method of (Tsuji, 2001).
Apart from Japanese, (Jeong et al., 1999) proposeda method to identify the original English words for
Korean words based on transliteration. Our method issimilar to theirs in using and combining all the transliteration rules observed in the training corpora. The
main di erence is that while they use transition probabilities from one bi-gram (which composes the word)
to another, we do not consider such transitions. We
assume that the Japanese loan word and the French
word correspond on mora-basis and occurence of thecorresponding units does not depend on the previousunits. This assumption is also practically important.
Using the transition probability will lead to the problem of data sparseness.
5.

Extraction Result

We used precision and recall to measure the result.
The precision is the ratio of correct pairs extracted
against pairs extracted. And the recall is the ratioof correct pairs extracted against correct pairs in thewhole corpus.
The precision and recall of our method are shownin Figures 1. In this gure, `J-French' represents theresult of our method based on Katakana-French TR.
`J-English' represents the result of our method basedon Katakana-English TR.
From this gure, we can say that the method basedon Katakana-French TR performed reasonably well,achieving 80% precision at 20% recall. Besides it is observed that the e ectiveness of using Katakana-French
TR and using Katakana-English TR do not di er signi cantly. We examined the actual pairs but could
not nd any tendency which might have produced thedi erence.
6.

4.

Related Studies

The methods to extract Japanese Katakana word
and French word pairs from bilingual corpora basedon transliteration has not been proposed so far. The
methods to extract Japanese-English loan word pairs
from bilingual corpora were proposed in (Ishimoto andNagao, 1994), (Kumano, 1995), (Matsuo and Shirai,Conclusions

It can be said that our method based on KatakanaFrench TR performed well considering that not a few
Katakana-French pairs do not correspond in view oftransliteration. We can use frequency-based method toextract these non-corresponding pairs. The integrationof transliteration-based method and frequency-based
method is promising.

We obtained interesting result that KatakanaEnglish TR performed well against Katakana-Frenchpair extraction. It is worth examining whether theKatakana-English TR works also well against Japaneseand the other language such as German and Spanish.
The weak point of this study is that the size of thecorpus we used is quite small and the number of pairs
from which we constructed TR is also small. We would
like to enlarge the size of them and con rm the result.
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100J-French
J-English

80

Precision(%)

J.

7.

6040200020406080

100

Recall(%)

Figure 1: Extraction Result based on Katakana-Frenchand Katakana-English TR