Detecting Transliterated Orthographic Variants
via Two Similarity Metrics
Kiyonori Ohtake
ATR SLTKeihanna Science City
Kyoto 619-0288,
Japankiyonori.ohtake@atr.jp

Youichi Sekiguchi
Nagaoka Univ. of Tech.
Nagaoka City,
Niigata 940-2188,
Japan
sekiguti@nlp.nagaokaut.ac.jp

Abstract
We propose a detection method for orthographic variants caused by transliteration in a large corpus. The method
employs two similarities. One is string
similarity based on edit distance. The
other is contextual similarity by a vector space model. Experimental results
show that the method performed a 0.889
F-measure in an open test.

1

Introduction

This paper discusses a detection method fortransliterated orthographic variants of foreign
words. Transliteration of foreign words causes
orthographic variants because there are several
conditions required for transliterating. One may
person transliterate to approximate pronunciation,
whereas another one may conduct transliteration
based on spelling. For example, the English
word report can be transliterated into two Japanesewords, “リポート (ripooto)” and “レポート (repooto).” The former “ripooto” is based on an approximation of its pronunciation, while “repooto”
is transliterated from its spelling.
In addition, several source languages can betransliterated. For instance, the English word virus
corresponds to the Japanese words: “ ウイルス(uirusu)” from Latin, “ビールス (biirusu)” and “
ヴィールス (viirusu)” from German, while “バイラス (bairasu)” or “ヴァイラス (vairasu)” arealso possible as transliterations that approximate
the English pronunciation. Moreover, some foreign words end up in different forms in Japanese
because of variation in English pronunciation; e.g.,
between British and American. For example, theKazuhide Yamamoto
Nagaoka Univ. of Tech.
Nagaoka City,
Niigata 940-2188,
Japan
yamamoto@fw.ipsj.or.jp

English word body corresponds to two words: “ ボディ (bodi)” from British and “バディ (badi)” from
American.
One may think that if back-transliteration weredone precisely, those variants would be backtransliterated into one word, and they would
be recognized as variants.
However, backtransliteration is known to be a very difficulttask(Knight and Graehl, 1997).
Not only Japanese but any language that has aphonetic spelling has this problem of transliterated orthographic variants. For example, English
has variants for a Chinese proper noun as “Shanhaiguan,” “Shanhaikwan,” or “Shanhaikuan.”Nowadays, it is well recognized that orthographic variant correction is an important processing step for achieving high performance in natural language processing. In order to achieve robust and reliable processing, we have to use many
language resources: many types of corpora, dictionaries, thesauri, and so on. Orthographic variants cause many mismatches at any stage of natural
language processing. In addition, not only orthographic variants, but also misspelled words tend toslip into a corpus. These words boost the perplexity of the corpus, and worsen the data sparsenessproblem.
To date, several studies have tried to cope withthis orthographic variant problem; however, they
considered the problem in a relatively clean corpusthat was well organized by natives of the target language. As with orthographic variants, misspelledwords cause mismatches, and we have to detect
not only predictable orthographic variants but also
misspelled variants. In addition, it is very hard to
detect orthographic variants caused by misspellingwith ordinary rule-based methods, because preparing such rules for misspellings that might be writ-709

ten is an unrealistic approach.
If a corpus includes texts that were written by
non-natives of the language, orthographic variants
that are misspelled will likely be increased because
non-natives have a limited vocabulary in that language.
We propose a robust detection method fortransliterated orthographic variants in a Japanese
corpus. The method is marked by a combination of different types of similarities. It is not
such a difficult task to detect simple misspelledwords, because a large dictionary would tell us
whether the word is common as long as we prepare a large enough dictionary. However, it often
occurs that a misspelled word is recognized as acommon word. For example, in English, someone
may mistype “from” as “form,” and string information will tell us nothing because both words arecommon. Therefore, we use contextual information to detect this kind of mistyping.

2

Transliteration for foreign words inJapanese: katakana

Japanese features three types of characters(katakana, hiragana, and kanji (Chinese characters)). Katakana is a syllabary which is usedmostly to write Western loanwords, onomatopoeicwords, names of plants and animals, non-Japanesepersonal and place names, for emphasis, and forslang, while hiragana is an ordinary syllabary.
Katakana cannot express the precise pronunciation of loanwords, because the katakana transliteration of a loanword is an attempt to approximate
the pronunciation of its etymon (the foreign wordfrom which it is derived). Thus, katakana orthography is often irregular, thus the same word may
be written in multiple ways. Although there are
general guidelines for loanword orthography, inpractice there is considerable variation. In addition, recent years have seen an enormous increasein katakana use, not only in technical terminology,
but in common daily usage.
To date, several detecting methods have been
proposed for English and other languages. One
may think that such methods can be applied toJapanese and work well. However, most katakana
characters correspond to two phonemes, and thiscauses several problems. Due to the correspondence between katakana characters and phonemes,
it is easy to imagine that the application would require tangled procedures.

2.1

Romanization

We use Japanese romanization for katakana characters to capture its pronunciation because there
are several katakana characters for which the pronunciation is the same. For example, “ ジ (possible romanization: zi/ji)” and “ヂ (possible romanization: di/zi)” are not differentiated in pronunciation. In addition, there are several katakana
expressions that have very similar pronunciations.
For instance, “チ (possible romanization: chi/ti)”in “チケット (ticket)” and “ティ (t’i)” in “ティケット (ticket)” are similar in pronunciation andthey cause these variants. Naturally we can use
katakana characters to compare two strings, but
employing katakana characters makes the comparing procedure cumbersome and complicated. To
avoid the complicated comparing procedure forkatakana expressions, we use Japanese romanization.
We used a system of Japanese romanization
based on ISO 3602, the romanization of which isbased on the Kunreisiki system. There are two
major systems for Japanese romanization. One isbased on the theory of the Kunrei (Kunreisiki) system. The other one is the Hepburn system, which
is widely used in English-speaking communities.
The Kunreisiki system was designed to representkana morphology accurately. For example, thekatakana character “シ” is written as “si” in the
Kunreisiki system while it is written as “shi” inthe Hepburn system. In this example, the character “h” that is inserted disturbs simple matchingprocedures, because most katakana characters correspond to two romanized characters: a consonant
and a vowel. Thus we prefer to use a romanization
system based on the Kunreisiki system to make thematching procedure simple.

3 Detecting method
We propose a detecting method for katakana variants. The method consists of two components: oneis string similarity and the other is contextual similarity.
The string similarity part measures similarity
based on edit distance for katakana words. Moreprecisely, there are two metrics of similarity: one
measures the similarity between romanized stringsof two words, while the other measures the similarity between raw strings of two words. We cannot use the romanization system as a perfect substitution of katakana, because romanization causes
side effects. For example, both Japanese words “ パ710

5. Calculate string similarity sim s between the
input and each candidate. If the input and acandidate word share a suffix or prefix morphemes, the shared morphemes would be excluded in the same way as above.

ンチ (punch)” and “パンティ(panty)” are transliterated into “panti” by our romanization system.
Thus, we use two string similarities.
Contextual similarity is defined as the distance
between context vectors. A context vector we employed is marked by using a dependency structure of a sentence. A context vector is constructedby gathering surrounding information for the target katakana word, such as cooccurring nouns,
the predicate expression depended upon by thekatakana word, the particle marking the katakana
word, and so on.
CorpusKatakana
words
ripootorepootouirusuviirusu. . .

analyze

Dependency
Analyzed
Corpus

7. Decide whether the candidate is a variant bymeans of a deciding module. The deciding
module follows the decision list showed inTable 1, where we used the Gakken Kokugo
Daijiten as the dictionary. It has almost 8,000
katakana words, and we slightly modified it.
We explain the details of the string similarity
part and the contextual similarity part in the following subsections.

extract

Contextual
Vectorsvec( ripooto )vec( repooto)
stringuirusu ) contextual
similarity vec(
similarity
vec( viirusu )

3.1

. . .

score > threshold ?
detected variant: ripooto

6. Calculate the contextual similarity sim c between the input and each candidate.

repootoFigure 1: Overview of detecting katakana variants
Figure 1 shows an overview of the detecting
method. The detection procedure is as follows:
1. Extract katakana words and contextual vectors from the dependency-analyzed result of
the target corpus.
2. Choose a katakana word as the input wordfrom the extracted katakana words.
3. Retrieve candidates of katakana variants fromthe extracted katakana words. Each candidate
should share at least one character with the
input word.
4. Calculate the similarity simed , which is basedon the ordinary edit distance, between the input Str1 and each candidate Str2 . The similarity simed is defined as follows:
ED(Str1 , Str2 ),simed (Str1 , Str2 ) = 1 −|Str1 | + |Str2 |(1)
where ED(Str1 , Str2 ) denotes the ordinary
edit distance. If the input and a candidate
word share suffix or prefix morphemes, theshared morphemes would be excluded fromthe comparing strings.

String similarity for romanized words

There are recognizable patterns in Japanesetransliterated orthographic variants, thus, so far
several rule-based methods to detect such variants have been developed. We use a kind ofweighted edit distance to recognize transliterated
orthographic variants. The weighting rules arevery similar to the rules that are used in conventional rule-based methods. The ordinary edit distance between two strings is defined as the numberof edit operations (insertion, deletion, and substitution, although sometimes substitution is not permitted) required to edit from one string into theother. Thus, the ordinary edit distance is a positive
integer value. We defined small weighted operations in specific situations to identify recognizable
patterns. Figure 2 shows an example of the difference between ordinary edit distance and weighted
edit distance, in which we used a rule for changing
a vowel that follows the same consonant (‘r’).
リポート
r i p o o t osubstitution (or deletion and insertion)r e p o o t oレポートordinary edit distance=1 (or 2)
weighted edit distance=0.8

Figure 2: Example of ordinary edit distance andweighted edit distanceMore precisely, string similarity based on
the weighted edit distance of romanizations for711

lengthfrequency
simed
sims
simc
> T Hlen∗> T Hed1 > T Hst1∗<= T Hlen > T Hf req∗∗< T Hcos1< T Hlen∗∗∗> T Hcos2
Both words have entries in pre-defined dictionary∗∗> T Hed2 > T Hst2
∗
∗
∗
∗
∗
∗
‘∗’ means any conditions.

decisionvariant
not variant
variant
not variant
variant
not variant

Table 1: Decision list of deciding modulekatakana words A and B is defined as Formula (2),

3.2

EDk (rom(A), rom(B))
Sims (A, B) = 1 −,|rom(A)| + |rom(B)|(2)
where rom(x) denotes romanized strings of x, andEDk (x, y) denotes a weighted edit distance between x and y that is specialized for katakana.
EDk (x, y) is a kind of weighted edit distance,
and is marked by a distance function that determines the relaxed distance based on local strings.
Here, EDk (x, y) is defined as Formula (3),In order to use the contextual information surrounding a katakana word, we employed a vector space model. We use a dependency analyzer
to achieve more precise similarity, and contextual
information is extracted from the dependency analyzed result of the text. Figure 3 shows an example
of extracting vectors from an analyzed result, inwhich the vector has elements; N for cooccurring
noun, P for predicate expression that is depended
upon by the word, and PP for the particle and predicate expression pairs.

EDk (x, y) = D(|x|, |y|),(3)

where, for two strings S1 and S2 , D(i, j) is defined
to be the specialized edit distance of S 1 [1..i] andS2 [1..j].
D(i, j) is given by the following recurrence relation:D(i − 1, j) + id(i, j),
D(i, j) = min  D(i − 1, j − 1) + 2t(i, j),  ,D(i, j − 1) + id(i, j)(4)where id(i, j) defines the insertion and deletion
operation distance, and that is defined to havethe penalty value Pid if S1 (i) or S2 (j) denotes
a consonant, and id(i, j) has the value 1 in allother cases. In addition, t(i, j) defines the substitution operation distance, and that is defined tohave the value 0 if S1 (i) = S2 (j), in all othercases, t(i, j) has a pre-defined table and returns avalue that depends on S1 [i − 3, .., i, .., i + 3] andS2 [j − 3, .., j, .., j + 3].
Table 2 shows an example of part of the t(i, j)table. There are 29 entries in the t(i, j) table. In
Table 2, several t(i, j) values are negative because
in such a situation the strings compared have already had or will have a positive distance, thus thet(i, j) has a negative value to adjust the distance.
Contextual similarity

syanpen ogurasu dekudasai.
(basic form iskudasaru)
ください

シャンペンをグラスで(A glass of champagne, please.)vec(syanpen) =[N;gurasu:1, P;kudasaru:1, PP;o-kudasaru:1]
vec(gurasu) =[N;syanpen:1, P;kudasaru:1, PP;de-kudasaru:1]

Figure 3: Extracting vector from dependencyanalyzed result
The vectors are calculated by the following procedure.
1. Analyze the dependency structure for all sentences of the target corpus. We employed
CaboCha1 as the dependency analyzer.
2. Extract vectors for all katakana words included in the corpus. Each vector corresponds
to a katakana word and consists of the following elements:• Nouns that cooccur with the katakana
word.
1
http://chasen.org/˜taku/software/cabocha/

712

i−3
j−3
∗
∗
y
∗
∗
∗

i−2
j−2
∗
∗
u
i
∗
∗

i−1
i
i+1
i+2 i+3
j−1
jj+1
j+2 j+3S2 (j − 1)yu
[kmnt]i
S1 (i − 1)i
S1 (i + 2)yu[kmnt]iu∗∗
S1 (i − 1)
y
u
∗
∗
∗
[dz]
i
∗
∗
∗
[dz]
i
∗
∗
‘∗’ means any character.
‘[ ]’ means character class in a regular expression.

t(i, j)
-0.3
-0.3
0.25

Table 2: A part of t(i, j) table• Predicate that is depended upon by thekatakana word.
• Particle and predicate pair: particle that
follows the katakana word and predicatethat is depended upon by the katakana
word.
Each element is extracted from thedependency-analyzed result of a sentence,and the frequency of the element is counted.
3. Load a tf-idf-like weight onto each elementof the vector. The weight is calculated by thefollowing formula.
N
W (kwi , ei ) = f (kwi , ei ) log.
sf (kwi )(5)Here, kwi is a katakana word, ei is an element of the vector corresponding to kw i ,f (kwi , ei ) denotes the frequency of the element ei for kwi , sf (kwi ) denotes the frequency of the sentence including kw i , and N
denotes the number of katakana words in the
corpus.
The contextual similarity is defined as the following formula.
simc (kwi , kwj ) = cos(vec(kwi ), vec(kwj ))= qP

Pex W (kwi , ex )W (kwj , ex )em W (kwi , em )2qP

en W (kwj , en )

,
2

(6)
where vec(kw) denotes a vector corresponding to
the katakana word kw.

4

Experiments

We used the ATR Basic Travel Expression Corpus (BTEC)(Takezawa et al., 2002) as a resource

for text. BTEC is a multilingual corpus and was
mainly developed with English and Japanese. The
Japanese part of BTEC contains not only ordinary katakana variants, but also mis-transliterated
katakana strings by non-Japanese natives that serveas our target for detection. The BTEC we usedconsists of almost 200,000 sentences.
We used almost 160,000 sentences for the development of the t(i, j) table, other rules usedin our method, and parameter estimations for the
method. We manually estimated the parameters
to achieve the highest F-measure for the development sentences, and estimated the parameters as
follows: Pid = 2.5, T Hlen = 5, T Hst1 = 0.94,
T Hf req = 3, T Hcos1 = 0.12, T Hcos2 = 0.02,
T Hed = 0.65, and T Hst2 = 0.89.
The developmental corpus includes almost
6,000 types of katakana words. We carried out
a closed test using the development corpus withthese parameter settings. There are two choices
for the detection method: One is the use of a dictionary to judge whether the input and candidate
words are known as different words. The otheris the use of contextual similarity. Actually, inthe detection method, contextual similarity plays
a supportive role because there is a data sparsenessproblem. Therefore we carried out an experimentwith four conditions. Table 3 shows the results ofrecall, precision, and F-measure on these four conditions.
The remaining 40,000 sentences were used as atest set, with which we carried out an open test.
Table 4 shows the result of the open test.
There is an obvious tendency for the detectionof short words to be very difficult. We comparedan F-measure for each class of word length, with
Figure 4 showing the results in open tests with thedictionary and without it. Both open tests wereconducted without contextual similarity. Compar-

713

Recall
Precision
Fwith dictionary, with context
0.820 (296/361) 0.931 (296/318) 0.872
with dictionary, without context
0.850 (307/361) 0.930 (307/330) 0.889
without dictionary, with context
0.823 (297/361) 0.903 (297/329) 0.861
without dictionary, without context
0.850 (307/361) 0.862 (307/356) 0.856
Table 3: Closed test results
RecallPrecision
Fwith dictionary, with context
0.827 (62/75) 0.886 (62/70) 0.855
with dictionary, without context
0.907 (68/75) 0.872 (68/78) 0.889
without dictionary, with context
0.800 (60/75) 0.822 (60/73) 0.811
without dictionary, without context
0.880 (66/75) 0.725 (66/91) 0.795
Table 4: Open test results
ing these results tells us the effect of the introduced
dictionary.
F-measure
1

0.9

0.8

with dictionary

0.7

without dictionary

0.6

0.5

2

4

6

8

10

12

14

word length

Figure 4: Results of open test with dictionary andwithout dictionary in each word length

5

Discussion

The experimental results showed that it is very difficult to detect short variants. Thus, it is reasonable
to use a dictionary for words that we already know.
Figure 4 shows the impact of the dictionary.
Most of the detection errors are related to proper

nouns, because there are many proper nouns thatare difficult to recognize as different words, suchas “マリー (marii, Mary)” and “マリア (maria,
Maria),” and so on. Furthermore, it is also hardto differentiate these words by their contextual
vectors, because using proper nouns is independent of context. If we can precisely detect theseproper nouns written in katakana, we will be able
to avoid such mis-detection. In practical situations,an enormous dictionary of proper nouns such as
ENAMDICT2 would be useful for this problem.
The detection method successfully detected several mistyped words, such as “ブラン (buran)” for“ブラウン (buraun, Brown),” “フリーモントホ
テル (furiimonto hoteru)” for “フレモントホテル (furemonto hoteru, the Fremont Hotel),” and soon. Most of the detected mistypes were vowels,because the string similarity is designed to be tolerant of mistyped vowels, and such mistypes weredetected successfully. However, it seems to be adifficult task to detect mistypes with consonants,
because, in ordinary situations, most mistypes related to consonants seem to be completely different
words.
In addition, there are several variants that are
difficult to detect by this method. A typical example was shown in the Introduction: “ ウイルス(uirusu)” or “ビールス (biirusu)” for the English
word virus. This type of variant includes drasticchanges; for instance, the ordinary edit distance
between “ウイルス (uirusu)” and “ビールス (biirusu)” is four, and the similarity derived from the
distance is too small (0.5) to identify it as the sameword. Moreover, there is another type of orthographic variant that has changed with time. BTEC
includes such an example: for the English word
milkshake, both “ミルクセーキ (mirukuseeki)”and “ミルクシェーク (mirukusyeeku)” exist. We
have to be careful of this problem when we processa corpus that has been developed for quite sometime, and that includes both very old texts and newones.
A well known problem arises here: data sparseness. Orthographic variants appear less frequently
than their standard expressions, and we cannot expect to have much contextual information for orthographic variants. Therefore, we always haveto cope with this problem even when we process
very large corpus, because the appearance of variants does not relate to the size of the corpus. The
2
http://www.csse.monash.edu.au/˜jwb/
enamdict doc.html

714

basic idea of the contextual vector seems very reasonable for words that appear frequently in a target
corpus. However, experimental results showed thatthe contextual similarity did not work as expected
because of this data sparseness. Consequently, toachieve reliable contextual similarity, we have to
use sentences in which a candidate of the variant isused. On-line WWW searching seems to be goodas such a resource for variant detection because
WWW texts include many variants.
On the other hand, there was a pair of words
that have very high string similarity and contextual
similarity, but they are not variants. That pair is“シャンペン (syanpen champagne): サインペン(sainpen sign pen / felt-tip pen),” and examples ofsentences that include each word are as follows:
syanpen o gurasu de kudasai. (Give me
a glass of champagne, please.)
sainpen o ippon kudasai. (Give me a felttip pen, please.)
Both words are arguments of the same verb “kudasai,” and the vectors derived from the analyzed
result of these sentences would be very similar.
Practically, these words are identified as different
words by using a dictionary. However, when using only contextual similarity, these words would
be judged as variants.
It is not easy to detect all of the variants by applying the proposed method. Indeed, the method
employs contextual information to achieve good
performance, but the contextual information usedalso includes variants, and the variants cause mismatches. In addition, not only katakana variants,
but also other orthographic variants, such as kanji
and cross-script orthographic variants (e.g., kanjivs. hiragana, hiragana vs. katakana, and so on),should be detected to achieve high precision andrecall.

6

Related works

To date, there have been several studies conductedon the detection of transliterated orthographic variants(e.g., (Kubota et al., 1994; Shishibori et al.,1994)). Most of these, however, targeted a relatively clean and well organized corpus or they
assumed artificial situations. As a practical matter, not only predictable orthographic variants but
also misspelled words should be detected. The detection of transliterated orthographic variants andspelling corrections have been studied separately,

and there is no study that is directly related to ourwork.
There are several studies on transliteration (e.g.,(Knight and Graehl, 1997)), and they tell us thatmachine transliteration of language pairs that employ very different alphabets and sound systems isextremely difficult, and that the technology is stillto immature for use in practical processing.

7 Conclusion
We propose a method to detect transliterated orthographic variants. The method is marked by the useof string similarity and contextual similarity via
contextual vectors. The method achieved a 0.889
F-measure in an open test. The results showed that
detection of short word variants is very difficult,and a dictionary raised the precision for such shortwords. However, contextual similarity did not contribute as expected to the detection of orthographic
variants.

Acknowledgements
This research was supported in part by the Ministry of Public Management, Home Affairs, Posts
and Telecommunications. The authors thank theanonymous reviewers for their suggestive and encouraging comments.

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