Translation Estimation for Technical Terms
using Corpus collected from the Web
Masatsugu Tonoike†, Mitsuhiro Kida†, Toshihiro Takagi†, Yasuhiro Sasaki†,Takehito Utsuro† andSatoshi Sato‡†Graduate School of Informatics,‡Graduate School of Engineering,
Kyoto University
Nagoya University
Yoshida-Honmachi, Sakyo-ku,
Furo-cho, Chikusa-ku,
Kyoto 606-8501 Japan
Nagoya 464-8603 JAPAN(tonoike,kida,takagi,sasaki, ssato@nuee.nagoya-u.ac.jp
utsuro)@pine.kuee.kyoto-u.ac.jp
Abstract
In the task of estimating bilingual term
correspondences of technical terms, it isusually quite difficult to find an existing
corpus for the domain of such technicalterms. In this paper, we take an approachof collecting the corpus for the domainof such technical terms from the Web.
This paper proposes a method of compositional translation estimation for technical terms, and through experimental evaluation, shows that domain/topic specificcorpus contributes to improving the performance of compositional translation estimation.
Keywords “language translation”, “corpora andcorpus-based language processing”, “electronic dictionary, thesaurus and ontology”

1 Introduction
This paper studies issues on compiling a bilingual
lexicon for technical terms. So far, several techniques of estimating bilingual term correspondencesfrom parallel/comparable corpus have been studied(Matsumoto and Utsuro, 2000). However, there
are a limited number of parallel/comparable corpora that are available for the purpose of estimating
bilingual term correspondences. Therefore, even if
one wants to apply those existing techniques to thetask of estimating bilingual term correspondences oftechnical terms, it is usually quite difficult to findexisting bilingual lexicon
processdata

sample termsof specificdomain/topic(language S )collecting termsof specificdomain/topic(language S )looking up
bilingual lexicon

web
web(language(languageTT))

term set(language S )XS (translations
are known)

XT(language T )YS (translations
are unknown)translation set(language T )estimating bilingual term
correspondenceswebweb
validating
language pair (S,T )(language(languageSS))translation
candidates
YST
new bilingual lexicon

collecting
corpus(language T )domain/topic
specific
corpus(language T )Figure 1: Compilation of Domain/Topic Specific
Bilingual Lexicon

an existing corpus for the domain of such technicalterms.
Considering such a situation, we take an approachof collecting a corpus for the domain of such technical terms from the Web. In this approach, in order to compile bilingual lexicon for technical terms,
the following two issues have to be addressed: collecting technical terms to be listed as headwords ofbilingual lexicon, and estimating translation of thosetechnical terms. Among those two issues, this paper
focuses on the second issue of translation estimation of technical terms, and proposes a method fortranslation estimation for technical terms using domain/topic specific corpus collected from the Web.
More specifically, the overall framework of compiling bilingual lexicon from the Web can be illustrated as in Figure 1. Suppose that we have sample
terms of a specific domain/topic, technical terms tobe listed as headwords of bilingual lexicon are collected from the Web by the related term collection
method of (Sato and Sasaki, 2003). Those collected
technical terms can be divided into two subsets according to whether or not the term can be translatedwith an existing bilingual lexicon (i.e. the subset
XS of terms whose translation are known, and thesubset YS of terms whose translation are unknown).
For those terms of the subset YS which can not betranslated with an existing bilingual lexicon, bilingual term correspondences are estimated using domain/topic specific corpus. Here, in order to collectdomain/topic specific corpus from the Web, we use
the set XT of the translation of the terms in the set
XS .
As a method of translation estimation for technical terms, we propose a compositional translation
estimation technique. Compositional translation estimation of a term can be done through the process ofcompositionally generating translation candidates of
the term by concatenating the translation of the constituents of the term. Here, those translation candidates are validated using domain/topic specific corpus.
In order to assess the applicability of the compositional translation estimation technique, we randomly
pick up 667 Japanese and English technical termtranslation pairs of 10 domains from existing technical term bilingual lexicons. We then manually examine their compositionality, and find out that 88% ofthem are actually compositional, which is a very encouraging result. Based on this assessment, this paper proposes a method of compositional translation
estimation for technical terms, and through experimental evaluation, shows that domain/topic specificcorpus contributes to improving the performance ofcompositional translation estimation.

2 Collecting Domain/Topic Specific
CorpusAs introduced in the previous section, XS denotes
the set of terms which can be translated with an existing bilingual lexicon, and XT the set of the translation of the terms in the set XS . When collecting domain/topic specific corpus of the language T ,for each technical term xT in the set XT , we collect the top 100 pages with search engine queries

including xT . Our search engine queries are designed so that documents which describe the technical term xt is to be ranked high. For example,
an online glossary is one of such documents. Notethat the queries in English and those in Japanese do
not correspond. When collecting Japanese corpus,
search engine “goo”1 is used. The specific queries
used here are phrases with topic-marking postpositional particles such as “xT とは”, “xT という”, “xTは”, and an adnominal phrase “xT の”, and “xT ”.
When collecting English corpus, search engine “AltaVista”2 is used. The specific queries used here are“xT ”, “xT AND what’s”, and “xT AND glossary”“xT ”.

3 Compositional Translation Estimation
for Technical Terms
3.1

Overview

An example of compositional translation estimation
for the Japanese technical term “応用行動分析” isshown in Figure 2. First, the Japanese technicalterm “応用行動分析” is decomposed into its constituents by consulting an existing bilingual lexicon
and retrieving Japanese headwords.3 In this case,the result of this decomposition can be given as in
the cases “a” and “b” in Figure 2. Then, each constituent is translated into the target language. A confidence score is assigned to the translation of eachconstituent. Finally, translation candidates are generated by concatenating the translation of those constituents without changing word order. The confidence score of translation candidates are defined as
the product of the confidence scores of each constituent. Here, when validating those translation
candidates using domain/topic specific corpus, thosewhich are not observed in the corpus are not regarded as candidates.
3.2

Compiling Bilingual Constituents Lexicon

This section describes how to compile bilingual constituents lexicons from the translation pairs of the1

http://www.goo.ne.jp/
http://www.altavista.com/
3
Here, as an existing bilingual lexicon, we use “Eijiro”(http://www.alc.co.jp/) and bilingual constituents lexicons
compiled from the translation pairs of “Eijiro” (details to be described in the next section)2

䋱䋮Decompose source term into constituentsa ᔕ↪
• application(1)• practical(0.3)• applied(1.6)

b ᔕ↪
• application(1)• practical(0.3)• applied(1.6)

Generated translation candidatesⴕേappliedappliedappliedprocess

䋲䋮Translate constituents into target languageಽᨆ• action(1)• activity(1)• behavior(1)• analysis(1)• diagnosis(1)• assay(0.3)ⴕേಽᨆ• behavior analysis(10)䋳䋮Compositional generationof translation candidate• applied behavior analysis(17.6) 㸢(1.6㬍1㬍1)+(1.6㬍10)• application behavior analysis(11)• applied behavior diagnosis(1)Figure 2: Compositional Translation Estimation for
the Japanese Technical Term “応用行動分析”

appliedmathematics
science
robot

:
:
:
..
.
⇓
:

応用
応用
応用

数学
科学
ロボット

応用

frequency
↓
: 40





Figure 3: Example of Estimating Bilingual Constituents Translation Pair (Prefix)
Table 1: Numbers of Entries and Translation Pairs
in Lexicons# of entries# of translation
EnglishJapanesepairs
Eijiro1,292,117 1,228,750
1,671,230
P2
232,716
200,633
258,211
BP
38,35338,546
112,586BS
22,281
20,62771,429Eijiro : existing bilingual lexicon
P2 : entries of Eijiro with two constituentsin both languages
BP : bilingual constituents lexicon (prefix)
BS : bilingual constituents lexicon (suffix)
lexiconexisting bilingual lexicon “Eijiro”. The underlying idea of augmenting the existing bilingual lexicon with bilingual constituents lexicons is illustrated
with the example of Figure 3. Suppose that the existing bilingual lexicon does not include a translation
pair “applied : 応用”, while it includes many compound translation pairs with the first English word
as “applied” and the first Japanese word “応用”.4 In
such a case, we align those translation pairs and estimate a bilingual constituent translation pair, whichis to be collected into bilingual constituents lexicon.
More specifically, from the existing bilingual lexicon, we first collect translation pairs whose English
terms and Japanese terms consist of two constituents
into another lexicon P2 . We compile “bilingual constituents lexicon (prefix)” from the first constituentsof the translation pairs in P2 and compile “bilingual
constituents lexicon (suffix)” from their second constituents. The numbers of entries in each language
and those of translation pairs in those lexicons areshown in Table 1.
In the result of our assessment, only 27% of the
667 translation pairs mentioned in Section 1 can becompositionally generated using “Eijiro”, while therate increases up to 49% using both “Eijiro” and“bilingual constituents lexicons”.5
4
Japanese entries are supposed to be segmented into
a sequence of words by morphological analyzer JUMAN(http://www.kc.t.u-tokyo.ac.jp/nl-resource/juman.html)
5
In our rough estimation, the upper bound of this rate isabout 80%. Improvement from 49% to 80% could be achievedby extending the bilingual constituents lexicons and by introducing constituent reordering rules with prepositions into the

3.3

Score of Translation Pairs in Lexicon

This section introduces the confidence score oftranslation pairs in various lexicons presented in theprevious section. Here, we suppose that the translation pair s, t of the terms s and t is used whenestimating translation from the language of the terms to that of the term t. First, in this paper, we assume
that translation pairs follow certain preference rules
and can be ordered as below:
1. Translation pairs s, t in the existing bilingual
lexicon “Eijiro”, where the term s consists of
two or more constituents.
2. Translation pairs in bilingual constituents lexicons whose frequencies in P2 are high.
3. Translation pairs s, t in the existing bilingual
lexicon “Eijiro”, where the term s consists ofexactly one constituent.
4. Translation pairs in bilingual constituents lexicons whose frequencies in P2 are not high.
As the definition of the confidence score q(s, t)of the translation pair s, t, in this paper, we use theprocess of compositional translation candidate generation.

“応用行動分析” of English term “applied behavior analysis”. Suppose that this translation candidate “応用行動分析” can be generated from two se⎧(compo(s)−1) (s, t in the existing⎪
10⎪
quences of translation pairs. One is the sequence⎪⎨lexicon)of a translation pair “applied”, “応用”  (with theq(s, t) =
⎪
f
(s,
t)
(s,
t
in
the
bilingual
log
⎪
10
score
1.6) and a translation pair “behavior analy⎪⎩
constituents lexicon) sis”, “行動 分析” (with the score 10), where the(1) score of “応用行動分析” can be calculated as 16
where compo(s) denotes the word (in English) or (= 1.6 × 10). The other is the sequence of a translamorpheme (in Japanese) count of s, and f (s, t) the tion pair “applied”, “応用” (with the score 1.6), afrequency of s, t in P2 . 6translation pair “behavior”, “行動” (with the score
Note that the score q(s, t) of a translation pair 1) and a translation pair “analysis”, “分析” (withs, t in either bilingual constituents lexicon whose the score 1), where the score of “応用行動分析” canf (s, t) is 1 becomes 0.
be calculated as 1.6 (= 1.6 × 1 × 1). Finally, the toFor example, the score of a translation pair tal score of “応用行動分析” is calculated as the sum
“behavior analysis”, “行動分析” in the existing 17.6 (= 16 + 1.6).
bilingual lexicon Eijiro is 10 (= 102−1 ). That ofa translation pair “applied”, “応用の” in the ex- 4 Translation Estimation using
isting bilingual lexicon Eijiro is 1 (= 101−1 ). That
Domain/topic Specific Corpus
of a translation pair “applied”, “応用” whose frequency as the first constituent in P2 is 40 is 1.6 It is not clear whether translation candidates which(= log10 40) in the bilingual constituents lexicon are generated by the method described in Section 3(prefix). That of a translation pair “applied”, “使 are valid as English or Japanese terms, and it is not
用” whose frequency as the first constituent in P2 also clear whether they belong to the domain/topic.
is 2 is 0.3 (= log10 2) in the bilingual constituents So using domain/topic specific corpus collected bylexicon (prefix).
the method described in Section 2, we examine
whether the translation candidates are valid as En3.4 Score of Translation Candidates
glish or Japanese terms and whether they belong toSuppose that a translation candidate yt is gener- the domain/topic. In our validation method, given aated from translation pairs s1 , t1 , · · · , sn , tn  as ranked list of translation candidates, each translationyt = t1 , · · · , tn . Here, in this paper, we define the candidate is checked whether it is observed in theconfidence score of yt as the product of the con- corpus, and one which is not observed in the corpus
fidence scores of the constituent translation pairs is removed from the list.
s1 , t1 , · · · , sn , tn .
As an example, we illustrate the case of estimating Japanese translation of an English technicaln
q(si , ti )(2) term “Euler function”. Figure 4 gives the list of theQ(yt ) =Japanese translation candidates for the English techi=1
nical term “Euler function” generated by the method
If a translation candidate is generated from more described in Section 3. The translation candidates
than one sequence of translation pairs, the score of are listed in descending order of the scores. The corthe translation candidate is defined as the sum of rect Japanese translation “オイラー関数” is ranked
the score of each sequence. For example, let us as the second highest. On the other hand, Figure 5
consider the score of Japanese translation candidate shows the result of validation of the translation candidates using “discrete mathematics” domain cor6
It is necessary to empirically examine whether this definipus. Translation candidates that are not observedtion of the confidence score is optimal or not. However, according to our rough qualitative examination, the results of confiin the corpus are removed from the list and only
dence scoring seem stable when without domain/topic specificthe correct Japanese translation “オイラー関数” recorpus, even with minor tuning by incorporating certain parameters into the score.
mains in the list.
following:Figure 5: Translation Estimation for the TechnicalTerm “Euler function” using “Discrete Mathematics” Domain Corpus
existing bilingual lexicon
processdata

sample termsof specificdomain/topic(language S )Figure 4: Translation Estimation for the TechnicalTerm “Euler function” without Domain/Topic Specific Corpus

collecting termsof specificdomain/topic(language S )looking up
bilingual lexicon

web
web(language(languageTT))

term set(language S )XS (translations
are known)

XT(language T )YS (translations
are unknown)translation set(language T )estimating bilingual term
correspondenceswebweb
validating
language pair (S,T )(language(languageSS))translation
candidates
YST
new bilingual lexicon

collecting
corpus(language T )domain/topic
specific
corpus(language T )5 Experiments and Evaluation
5.1

Translation Pairs for Evaluation

In our experimental evaluation, within the framework of compiling bilingual lexicon for technicalterms, we evaluate the translation estimation partwhich is indicated with bold line in Figure 6. In theevaluation of this paper, we simply skip the evaluation of the process of collecting technical terms to
be listed as headwords of bilingual lexicon. In order to evaluate the translation estimation part, fromten categories of existing Japanese-English technicalterm dictionaries listed in Table 2, translation pairs
s, t of two types are randomly picked up. Translation pairs of the first type are those which canbe found in the existing bilingual lexicon “Eijiro”,
which are collected into the set XST . Translation
pairs of the second type are those for which neithers nor t can be found in the existing bilingual lexicon “Eijiro”, which are collected into the set YST .
The set of terms XT taken from XST , that are foundin the side of the language T , is used for collecting domain/topic specific corpus from the Web, as
described in Section 1. Here, we suppose that trans-

Figure 6: Experimental Evaluation of Translation Estimation for Technical Terms with/without
Domain-specific Corpus (taken from Figure 1)
lation estimation evaluation is to be done against theset YS of terms of the language S taken from YST .
For each of the ten categories, Table 2 shows thesizes of XST and YST , respectively.
5.2

Translation Estimation for Technical Terms
with/without Domain-specific Corpus

Without domain specific corpus, the correct rateof first ranked translation candidate is 19% on the
average (both from English to Japanese and fromJapanese to English). The rate of including correct candidate within top 10 is 40% from English to
Japanese and 43% from Japanese to English on the
average. With domain specific corpus, on the average, the correct rate of first ranked translation candidate improved by 10% from English to Japaneseand by 7% from Japanese to English. However, therate of including correct candidate within top 10 de-McGraw-Hill
Iwanamicategories
Electromagnetics
Electrical engineering
OpticsProgramming language
Programming|XST |
73
72
71
61
63

|YST |
33
45
31
29
29

(Computer)

100

100

Dictionary ofComputer

Anatomical Terms
100
100
Disease100
100
Chemicals and Drugs
100
100
Physical Science
100
100
and Statistics
Total
840
667
McGraw-Hill : Dictionary of Scientific and Technical Terms
Iwanami : Encyclopedic Dictionary of Computer Science
Dictionary of
250,000
medical terms

creased by 3% from English to Japanese, and by 4%
from Japanese to English. This is because correcttranslation does not exist in the corpus for 20% of
the 667 translation pairs for evaluation.
For about 40% of the 667 translation pairs forevaluation, correct translation does exist in the corpus and can be generated through the compositionaltranslation estimation process. For those 40% translation pairs, Figure 7 compares the correct rate offirst ranked translation pairs between with/without
domain-specific corpus. The correct rates increaseby 26∼36% with domain-specific corpus. This result supports the claim that domain/topic specificcorpus is effective in translation estimation of technical terms.

6 Conclusion
This paper proposed a method of compositionaltranslation estimation for technical terms, andthrough experimental evaluation, showed that domain/topic specific corpus contributes to improving
the performance of compositional translation estimation.
As related works, (Cao and Li, 2002; Fujii and
Ishikawa, 2001) also proposed techniques of compositional estimation of bilingual term correspondences. One of the major differences of the techniques of (Cao and Li, 2002; Fujii and Ishikawa,
2001) and the one proposed in this paper is that weconcentrated on the translation estimation of technical terms. One of the other differences is the typeof corpus used to validate generated translation can-㪺㫆㫉㫉㪼㪺㫋㩷㫉㪸㫋㪼㩷㫆㪽㩷㪽㫀㫉㫊㫋㩷㫉㪸㫅㫂㪼㪻㩷㫋㫉㪸㫅㫊㫃㪸㫋㫀㫆㫅㪺㪸㫅㪻㫀㪻㪸㫋㪼㩷㩿㩼㪀㪜㫃㪼㪜㫃 㪺㫋㫉㪼㪺㫆㫋 㫉 㫄㪸㫀㪺㪸 㪾㫅㫃㩷㪼 㪼㫋㫅㪾 㫀㪺㫊
㫀㫅
㪧㫉
㪼㪼
㫆㪾
㫉㫀㫅
㫉㪸
㪾
㫄
㪦㫇
㫄
㫀㫅㪾
㫋
㩷㫃㪸 㫀㪺㫊㪧 㫉 㫅㪾 㫌㫆 㪾 㪸㪾㪼㫉㪸㫄㫄㪘㫅㪚㫆 㫀㫅㪾㪸㫋㫆 㫄 㫄㫇㫌㫋㫀㪺㪸㫃㩷 㪼㫉
㪫㪼
㪚㪿
㫉㫄
㪼㫄
㪛 㫊
㫀㪺
㪸㫃㫊 㫀㫊㪼
㪸㪧㪿 㩷㪸㫅㪻 㫊㪼㫐㫊㩷㫀㪺㪸 㪛㫉㫌㫃㩷㪪 㪾㫊
㪺㫀㪼
㫅㪺
㪘㫍 㪼
㪼㫉
㪸㪾
㪼

dictionaries

㪈㪇㪇
㪐㪇
㪏㪇
㪎㪇
㪍㪇
㪌㪇
㪋㪇
㪊㪇
㪉㪇
㪈㪇
㪇

㫎㫀㫋㪿㫆㫌㫋㩷㪺㫆㫉㫇㫌㫊㫎㫀㫋㪿㩷㪺㫆㫉㫇㫌㫊(a) English to Japanese㪺㫆㫉㫉㪼㪺㫋㩷㫉㪸㫋㪼㩷㫆㪽㩷㪽㫀㫉㫊㫋㩷㫉㪸㫅㫂㪼㪻㩷㫋㫉㪸㫅㫊㫃㪸㫋㫀㫆㫅㪺㪸㫅㪻㫀㪻㪸㫋㪼㩷㩿㩼㪀㪜㫃㪼㪜㫃 㪺㫋㫉㪼㪺㫆㫋 㫉 㫄㪸㫀㪺㪸 㪾㫅㫃㩷㪼 㪼㫋㫅㪾 㫀㪺㫊
㫀㫅
㪧㫉
㪼㪼
㫆㪾
㫉㫀㫅
㫉㪸
㪾
㫄
㪦㫇
㫄
㫀㫅㪾
㫋
㩷㫃㪸 㫀㪺㫊㪧 㫉 㫅㪾 㫌㫆 㪾 㪸㪾㪼㫉㪸㫄㫄㪘㫅㪚㫆 㫀㫅㪾㪸㫋㫆 㫄 㫄㫇㫌㫋㫀㪺㪸㫃㩷 㪼㫉
㪫㪼
㪚㪿
㫉㫄
㪼㫄
㪛 㫊
㫀㪺
㪸㫃㫊 㫀㫊㪼
㪸㪧㪿 㩷㪸㫅㪻 㫊㪼㫐㫊㩷㫀㪺㪸 㪛㫉㫌㫃㩷㪪 㪾㫊
㪺㫀㪼
㫅㪺
㪘㫍 㪼
㪼㫉
㪸㪾
㪼

Table 2: Number of Translation Pairs for Evaluation

㪈㪇㪇
㪐㪇
㪏㪇
㪎㪇
㪍㪇
㪌㪇
㪋㪇
㪊㪇
㪉㪇
㪈㪇
㪇

㫎㫀㫋㪿㫆㫌㫋㩷㪺㫆㫉㫇㫌㫊㫎㫀㫋㪿㩷㪺㫆㫉㫇㫌㫊(b) Japanese to English
Figure 7: Evaluation against the Translation Pairs
whose Correct Translation Exist in the Corpus andcan be Generated Compositionallydidates. (Cao and Li, 2002) use the whole Web as a
corpus, and (Fujii and Ishikawa, 2001) use corpus of
the collection of the technical papers, each of which
is published by one of the 65 Japanese associations
for various technical domains. Both can be regarded
as domain/topic independent corpora. On the other
hand, our method use a domain/topic specific corpus
corrected from the Web. If we use a domain/topic
independent corpus such as the Web to validate generated translation candidates, we might select translation candidates which are used in domains/topics
other than the target domains/topics. One of the merits of using a domain/topic specific corpus is that we
can select only translation candidates which are usedin the target domain/topic.
As a future work, we are planning to introduce a mechanism of re-ranking translation candidates based on frequencies of technical terms in domain/topic specific corpus.

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