Automatic Extraction of Translational Japanese-KATAKANA
and English Word Pairs from Bilingual Corpora
Keita Tsuji
Library and Information Science Course, Graduate School of Education, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, JAPAN
Email: keita@nii.ac.jp
Abstract
The method to automatically extract translational Japanese-KATAKANA and English word pairs frombilingual corpora is proposed. The method applies all the existing transliteration rules to each mora unit
in a KATAKANA word, and extract English word which matched or partially-matched to one of thesetransliteration candidates as translation. For instance, if there is a word ‘グラフ’ (graph) in Japanese partof bilingual corpora, we generate such transliteration candidates as <graf>, <graph>, <gulerph>,... andextract similar words from English part of corpora. The method worked fairly well, achieving 83-100%
precision at 75% recall against eight corpora we used for evaluation.
Keywords: transliteration, automatic construction, bilingual lexicon, bilingual corpora, KATAKANA.

1Introduction

We propose a method to automatically extract translational
Japanese-KATAKANA and English word pairs frombilingual corpora based on transliteration rules.
The bilingual lexicon is in demand in many fields such as
cross-language information retrieval and machinetranslation. And the bilingual corpora become widely
available reflecting the change in publishing activities. On
these background, the methods to construct bilingual
lexicon automatically based on bilingual corpora have been
intensively studied. But many of the methods proposed so
far depend heavily on word frequency in the corpora andcannot treat low-frequency words properly. The lowfrequency words include newly-coined words which areespecially in demand in many fields.
Reflecting the recent relationship between Japanese andEnglish environment, Japanese is now borrowing many
words from English. Thus the new words are often loan
words and are usually represented by KATAKANA
characters. Some methods to automatically extract
KATAKANA-English word pairs from bilingual corpora
have been proposed, but there is still much room for further
investigation.
Against these background, we propose a method to extract
translational KATAKANA-English word pairs frombilingual corpora. The method applies all the existingtransliteration rules to each mora unit in a KATAKANA
word, and extract English word which matched or partiallymatched to one of these transliteration candidates astranslation. For instance, if there is a word ‘グラフ’ (graph)in Japanese part of bilingual corpora, we generate suchtransliteration candidates as <graf>, <graph>, <gulerph>,...
and extract similar words from English part of corpora.
In the next section, we will explain our extraction method
in detail, together with some alternatives and related studies.
Next, we will show the results of our experiments anddiscuss the future direction.

2

MethodOur method extracts translational KATAKANA-English
word pairs from bilingual corpora based on transliteration
rules. In this section, we will first explain the way toconstruct the transliteration rules, and then the way to
extract KATAKANA-English translations from corpora.

2.1 Construction of Transliteration Rule
Based on a source list of KATAKANA-English word pairs(which can be obtained from Japanese-English dictionaries,etc.) and Hepburn transliteration rules, we construct thetransliteration rules as follows:(1) Decompose KATAKANA word in the source list into
mora units. For instance, the word ‘ディスパッチャ
ー’ is decomposed into four units: ‘ディ’, ‘ス’, ‘パッ’and ‘チャー’. 1(2) Based on the English counterpart word, extract
transliteration rule for each unit manually. For instance,from a pair ‘ディスパッチャー’ and ‘dispatcher’, we

1

We attached ‘ー’ to the preceding unit and regarded it as
one unit, though its number of mora is two.

can obtain the rules: ‘ディ’= ‘di’, ‘ス’= ‘s’, ‘パッ’=‘pat’ and ‘チャー’= ‘cher’. 2 3(3) Repeat (1) and (2) for all the word pairs in the list,
count the frequency of each rule (the number of times
observed in the list) and rank them for each
KATAKANA unit. The ranks are used for time-saving
as we will mention later.

w1 and w2 (e.g. S(‘guraf’, ‘graph’) = ‘gra’)Dice(k,m,n)=k*2/(m+n)
Extracting KATAKANA-English translations
bilingual corpora is achieved as follows:from(1) Pick up J and decompose it into units according to thesame framework we used at TR construction.

(4) Add Hepburn transliteration rules into the above rules,
with each frequency 1. This is done to supplement therules extracted from the source list. If the source list islarge enough, this process might not be necessary.

(2) Using all the transliteration rules in TR, generate all thepossible transliteration candidates for J. Henceforth we
represent the i-th transliteration candidate of J as Ti(J).

Henceforth we represent the rules obtained by the above
procedure as ‘TR’.

(3) Pick up E which co-occurred with J (occurred in thesame aligned segment in the corpus) and identify thelongest common subsequence with each Ti(J).

We assume the following four: (1) when Japanese borrows
an English word, the word is transliterated on the basis ofJapanese mora unit and their correspondences are stable,(2) these transliterations are free from context (have no
relation to the preceding or following mora units), (3) the
number of transliteration rules is small and (4) they do notvary drastically from time to time nor from domain todomain. Therefore, TR construction is a finite task andonce it is obtained, it can be used for a long period of timeand for various domains.

(4) If the following P(J, E) exceeds certain threshold,extract pair J and E as translation.
P(J, E)=maxi Dice(L(S(Ti(J), E)), L(Ti(J)), L(E))
In the case that TR is as in Table 1 and J is ‘グラフ’, 3*6*4transliteration candidates such as <graf>, <graph>,<graff>, ..., <gulerff> and <gulerfe> are obtained.
Therefore, in the case that E is ‘graph’:P(グラフ, graph)

2.2

Extraction of Traslational Word Pairs

In this section, we will explain our method to extract
KATAKANA-English translations from bilingual corpora.
First of all, the basic procedure which in fact iscomputationally prohibitive is described. And then its
countermeasure, the device for time-saving, is introduced.
The combination of the two, i.e., the procedure which ismodified to be fast enough is our method. Next, we will
explain some alternatives and methods in related studies.
2.2.1 Basic Procedure
First of all, we define the following symbols and functions.
J: KATAKANA word in the corpus
E: English word in the corpus
L(w): The number of characters of word w
T(J): The transliteration candidate of word J
S(w1, w2): The longest common subsequence of2

We used all the characters of English counterpart. The
characters which seem to have no corresponding Japanesepart are attached to the preceding characters. For instance,from a pair ‘ウェイト’ and ‘weight’, transliteration rule
‘イ’ and ‘igh’ is extracted.
3

We did not use English characters overlappingly. Only
exception is ‘x’. When ‘x’ corresponds to ‘クス’ (or‘キス’) , we extracted transliteration rules ‘ク’= ‘x’ and‘ス’= ‘x’, though it contradicts to our mora-basis
correspondence assumption.

= max( Dice(L(S(graf, graph)), L(graf), L(graph)),Dice(L(S(graph, graph)), L(graph), L(graph)),Dice(L(S(graff, graph)), L(graff), L(graph)),…
Dice(L(S(gulerfe, graph)), L(gulerfe), L(graph)))= max(0.67, 1.00, 0.60, …, 0.33, 0.33) = 1.00
It indicates that ‘グラフ’ and ‘graph’ are very likely to betranslation. On the other hand, in the case that E is ‘library’,P(J, E) is not high (P(グラフ, library)=max(0.36, 0.33, ...,
0.29, 0.29)=0.36), which indicates that they are not likely
to be translation.
Our basic idea is as follows. If we use all the transliteration
rules and generate all the possible transliteration candidates,‘correct’ transliteration is always in that set, though we do
not know which is the correct one. We use bilingual
corpora to resolve this, assuming that there exists atransliteration of KATAKANA word in English part ofbilingual corpora. We regard the transliteration candidates
which actually exist as words in English part as the correct
ones. However, the transliteration rules we obtain are
inclined to be insufficient. Therefore we do not use exact
match. Instead, we use Dice and extract the actual word in
the English part of corpora, not T(J), whose maximum ofDice is high, as translation.

グ
g
gue
gu

ラ
ra
la
l
lu
r
ler

フ
f
ph
ff
fe

Table 1: Transliteration rules

Mdic(k, m, n)=(1+logk)*k*2/(m+n)
Dice is simply a ratio between the length of thematched part and those of the words. For instance,
Dice(4,5,5)=Dice(8,10,10)=Dice(12,15,15)…
The
measures which emphasize the length of the matchedpart more might be more effective than Dice.
(2) The following Bgrm was used on behalf of the
combination of Dice and NPT_score:Bgrm(T(J), E)=|NT(J) ∩ NE|/|NT(J) ∪ NE|

2.2.2 Device for Time-saving
The previously-mentioned procedure requires muchcomputational time when applied to the 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 tolonger KATAKANA word. At TR construction, we haveranked transliteration rules according to their frequencies.
We applied top 12/(the number of units in J)+1 rules toeach unit of J. In the case that TR is as in Table 1 and J is
‘ グ ラ フ ’, the number of rules applied to each unit is
12/3+1=5. Therefore 3*5*4 transliteration candidates are
examined instead of 3*6*4 candidates.
As for (2), we used ‘NPT_score’ in [1] for computing
L(S(T(J), E)) . It is an abbreviated version for identifying
the longest common subsequences.

2.3

Other Alternatives

So far we have explained our method to extract
translational KATAKANA-English word pairs frombilingual corpora. To verify the effectiveness of our method,
we examined the following alternatives.
2.3.1 Transliteration Rule
The simple Hepburn transliteration rule (henceforth ‘HR’)
was used on behalf of TR. The purpose and perspective ofcomparing the effectiveness of TR and HR is as follows:(1) HR is an well-known rule for transliterating Japanese toalphabet strings and is easily available. If HR alone canproduce good result, we can save labor for constructing TR.
(2) HR gives each KATAKANA unit a unique alphabet
string. Thus, while TR usually generates many T(J), HR
generates only one T(J). If HR alone can produce goodresult, we can save computational time.
2.3.2 Measure for Matching
Our method uses the combination of (1) Dice and (2)NPT_score. But the other alternatives might be more
effective. From this view, we examined the following two:
(1) The following Mdic was used on behalf of Dice:

where Nw is a set of bi-grams which compose the word
w. For instance, Ngraph is ( _g, gr, ra, ap, ph, h_ ). Thismeasure was used in [2] which we will mention later.
2.3.3 Assumption of One-to-one Correspondence
The method to extract translational word pairs frombilingual corpora based on word frequency often assumes
one-to-one correspondence between words in one aligned
segment ([3] [4]). We examined whether the assumption is
also effective for our task.
We added the following extraction procedure to our method
and examined the performance improvement:(1) From one aligned segment, extract the pair whose Pvalue is the highest in the segment.
(2) Eliminate the words of that pair from the segment.
(3) If there remains KATAKANA word and English wordin the segment, go to (1), otherwise stop.
The procedure is applied against all the segments in the
corpus. Henceforth we will represent this procedure as ‘11P’ for simplicity.

2.4 Methods in Related Studies
The methods to extract Japanese-English loan word pairs
from bilingual corpora were proposed in [1][5][6][7]. But[5] did not go into details of transliteration method. [7]used only consonants for Japanese English matching. The
vowels which we think have useful information are ignored.
[6] extracted pairs whose estimated pronunciations matchedexactly. But the method to estimate the pronunciation ofEnglish word (which seems difficult) is not clearly
explained.
On the other hand, [1] did not discard vowels and showed
their procedure clearly. Thus we took up parts of their
method and compared the effectiveness with our method.
We extracted KATAKANA-English word pairs as follows:(1) Transliterate KATAKANA word into ‘NPT’ in [1], (2)Extract word pairs whose ‘Match’ are high.
[1] aimed at extracting proper names. Toward this goal, [1]excluded from candidates, English words whose first letters
are not in upper cases. But our extraction target is not
limited to proper names. Thus we did not use this and otheradditional rules used in [1] such as limiting the minimum
length of KATAKANA word.

Apart from Japanese, [2] proposed a method to identify theoriginal English words for Korean words based ontransliteration. Our method is similar to theirs in using andcombining all the transliteration rules observed in the
training corpora. The main difference 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 Japanese loan word and original English
word correspond on mora-basis and occurrences of thecorresponding units do not depend on the precedent units. 4

3

Experiment

In this section, we will first explain the data we used andthen the result of our method, other alternatives and themethod in related studies.

‘Ew’ indicates the number of tokens of English words. We
regarded one abstract (title) as one segment. Each corpus iscomposed of 1,000 segments except for abstract corpus offorestry (950 abstracts).
The purpose of using abstract and title corpora is to
examine the influence of size and the degree of parallelismof each segment to the extraction results. Abstracts arelarger and noisier than titles.
The purpose of using four domains is to examine theinfluence of domain difference to the results. In particular,
we constructed TR based on a dictionary of artificial
intelligence. If the results of the other three domains do notsignificantly differ from that of artificial intelligence, it canbe said that we need not be so nervous about the domain ofsource data for TR construction.

3.1 Data
The source data for TR construction and bilingual corpora
from which we extract translational pairs will be explained.

Abstract

3.1.1 Source Data for TR
We extracted transliteration rules from 3,742
transliterational term pairs in dictionary of artificial
intelligence [8] and added Hepburn transliteration rules tothem. The total number of rules in TR is 1,236. The numberof types of KATAKANA units in TR is 677. Thus, theaverage number of transliteration rules for one
KATAKANA unit is 1.83. The KATAKANA unit which has
transliteration rules most is ‘ク’, which has 13 rules (‘ク’=‘c’, ‘k’, ‘que’, ‘ke’,..).
3.1.2 Bilingual Corpora
To evaluate the performance of our method, eight bilingual
corpora were used. They are Japanese-English parallel
abstracts/titles of academic papers, which were made by theauthors of each paper. The domains of these corpora are
artificial intelligence (AI), forestry (FR), information
processing (IP) and architecture (AC). They were extracted
from the Database of Academic Conference Papers
provided by NACSIS.
The Japanese and English parts of these corpora areprocessed by morphological analyzer ChaSen2.0b and Brill
part-of-speech tagger respectively. We regarded thetranslational KATAKANA-English single-word noun pairs
in each segment of corpora as extraction target.
The basic quantities of the corpora are shown in Table 2. In
Table 2, ‘KEp’ indicates the number of types of theextraction target pairs. ‘Jw’ indicates the number of tokens
of ChaSen morphemes in the Japanese part of corpora and4This assumption is also practically important. Using thetransition probability will lead to the problem of data
sparseness.

Title

Domain
AI
FR
IP
AC
AI
FR
IP
AC

Jw
166,331
185,734
156,124
139,420
11,435
15,062
11,244
20,118Ew
126,101
113,789
106,753
74,776
9,625
14,575
9,153
17,200

KEp
493
470
728
348
227
161
267
169

Table 2: Basic Quantities of Bilingual Corpora

3.2 Results
The precision and recall of our extraction method, otheralternatives and the method in related studies are shown in
Figure 1-8. In these Figures, ‘TR_Dice_0’ represents theresult of our method. ‘HR_Dice_0’ represents the result ofusing HR on behalf of TR. ‘TR_Mdic_0’ represents theresult of using Mdic on behalf of Dice. ‘TR_Bgrm_0’
represents the result of using Bgrm on behalf of Dice and
NPT_score. ‘TR_Dice_1’ represents the result of our
method with 1-1P. ‘NR_Npts_0’ represents the result ofusing NPT and Match in [1].
From Figure 1-8, we can see the following:(1) TR is much more effective than simple Hepburn
transliteration rules. For instance, while ‘TR_Dice_0’
achieved 93% precision at 75% recall, the precision of‘HR_Dice_0’ at 75% recall remained 4% against theabstract corpora of artificial intelligence. Therefore TR
is worth constructing and using.
(3) Dice is more effective than Mdic. In bilingual corpora,
there are many word pairs which are long andmorphologically-related but not translational. Mdic
between these word pairs tend to become higher thanthose of short translational pairs, which leads to thedecrease of precision.

(3) The combination of Dice and NPT_score is more
effective than Bgrm. One apparent reason is that thelatter does not use the information of the bi-grams orderin original words.
(4) 1-1P slightly improves the precision at the expense ofrecall. Thus 1-1P can be a potential option to our
method under some circumstances or framework where
higher precision is important.
(5) Our method is more effective than using combination of
NPT and Match. The main reasons are as follows: (1)Compared with TR, the transliteration rule in [1] tends
to generate strings which contain incorrect translations.
For instance, a Japanese word ‘グラフ’ is transliterated
into ‘ghurlaoffphu’, which contains not only correcttranslation ‘graph’, but also incorrect one ‘hop’. (2)Unlike Dice, Match depends only on the length ofEnglish words and their matched parts. It does not use
information about the length of no-matched parts ingenerated strings. Therefore, it tends to evaluate short
English words which are contained by chance ingenerated strings as correct translations (for instance,
Match(‘ghurlaoffphu’, ‘hop’)=3/3=1).

4

Conclusion

We proposed a method for extracting translational
KATAKANA-English word pairs from bilingual corpora.
The experiment shows our method is highly effective. By
enriching TR and introducing some heuristics, theperformance will become higher.
Assuming that the low-frequency translational word pairs
are important extraction target and many of them arecomposed of KATAKANA words, we proposed the present
method. Our next task is to develop a method for extracting
the low-frequency non-KATAKANA and English
translations and high-frequency translations. We are now
investigating the integration of the present method and thestatistical extraction method based on word frequency.

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3(7) Our method achieved 96-100% precision at 75% recall
against title corpora. Though it was worse than that, ourmethod performed well against abstract corpora (8393% precision at 75% recall). Considering the highavailability of bilingual corpora aligned at abstract level,
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(6) TR which was constructed based on artificial
intelligence term pairs also performed well against theother three domains. This indicates the domainindependence of TR and that we do not have toconstruct TR for each domain.

Error Analysis

The extraction errors observed and countermeasures againstthem are as follows:・Most of the translational word pairs not extracted were
those containing transliteration units which were not listedin TR. For instance, ‘アーキテクチャ’ and ‘architecture’
was not extracted because ‘キ’ = ‘chi’，‘チャ’ = ‘ture’
were not listed in TR. We can solve this problem byenriching TR.
・A few of the translational word pairs which were notextracted were acronyms and their original forms. These
are translational KATAKANA-English word pairs, butcannot be extracted based on transliterations by nature.
・ Many of the non-translational word pairs wrongly
extracted were morphologically related pairs. For instance,‘ プ ロ グ ラ マ ’(programmer) and ‘program’ ， ‘ ク ラス’(class) and ‘subclass’ were extracted. Emphasizing thefirst and the last unit matching might be effective.

Figure 1: Abstract of Artificial Intelligence

Figure 5: Title of Artificial Intelligence

Figure 2: Abstract of Forestry

Figure 6: Title of Forestry

Figure 3: Abstract of Information Processing

Figure 7: Title of Information Processing

Figure 4: Abstract of Architecture

Figure 8: Title of Architecture