Semi-Automatic Identification of
Bilingual Synonymous Technical Terms
from Phrase Tables and Parallel Patent SentencesabbBing Liang , Takehito Utsuro , and Mikio Yamamotoa

Graduate School of Systems and Information Engineering,
University of Tsukuba, Tsukuba, 305-8573, JAPANb
Faculty of Engineering, Information and Systems,
University of Tsukuba, Tsukuba, 305-8573, JAPAN

Abstract. In the research field of machine translation of patent documents, the issue of acquiring technical term translation equivalent pairs automatically from parallel patent documents is one of those most important. We take an approach of utilizing the phrase table ofa state-of-the-art phrase-based statistical machine translation model. In this task, we consider situations where a technical term is observed in many parallel patent sentences andis translated into many translation equivalents. We apply SVM to the task of identifying
synonymous translation equivalent pairs and achieve almost 98% precision and over 40% Fmeasure. Then, in order to improve recall, we introduce a semi-automatic framework, where
we employ the strategy of selecting more than one seeds for each set of candidates bilingualsynonymous term pairs. By manually judging whether each pair of two seeds is synonymousor not, we achieve over 95% precision and 50% recall.
Keywords: Bilingual Lexicon Acquisition, Synonyms, Phrase Tables

1 Introduction
For both high quality machine and human translation, a large scale and high quality bilingual lexicon is the most important key resource. Since manual compilation of bilingual lexicon requires
plenty of time and huge manual labor, in the research area of knowledge acquisition from natural
language text, automatic bilingual lexicon compilation have been studied. Techniques invented so
far include translation term pair acquisition based on statistical co-occurrence measure from parallel sentences (Matsumoto and Utsuro, 2000), translation term pair acquisition from comparable
corpora (Fung and Yee, 1998), compositional translation generation based on an existing bilingual
lexicon for human use (Tonoike et al., 2006), and translation term pair acquisition by collecting
partially bilingual texts through the search engine (Huang et al., 2005).
Among those efforts of acquiring bilingual lexicon from text, Morishita et al. (2008) studied
to acquire technical term translation lexicon from phrase tables, which are trained by a phrasebased statistical machine translation model with parallel sentences automatically extracted fromparallel patent documents. Recently, we further studied to require the acquired technical termtranslation equivalents to be consistent with word alignment in parallel sentences and achieved
91.9% precision with almost 70% recall. This technique has been actually adopted by a Japaneseorganization which is responsible for translating Japanese patent applications published by theJapanese Patent Office (JPO) into English, where it has been utilized in the process of semiautomatically compiling bilingual technical term lexicon from parallel patent sentences. In thisprocess, persons who are working on compiling bilingual technical term lexicon judge whether toaccept or not candidates of bilingual technical term pairs presented by the system.
Copyright 2011 by Bing Liang, Takehito Utsuro, and Mikio Yamamoto
25th Pacific Asia Conference on Language, Information and Computation, pages 196–205

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Based on the achievement so far, in this paper, we consider situations where a technical term
is observed in many parallel patent sentences and is translated into many translation equivalents.
More specifically, in the task of acquiring technical term translation equivalent pairs, this paper
studies the issue of identifying synonymous translation equivalent pairs. First, we collect candidates of synonymous translation equivalent pairs from parallel patent sentences. Then, we analyzefeatures for identifying synonymous translation equivalent pairs. Finally, we apply the Support
Vector Machines (SVMs) (Vapnik, 1998) to the task of identifying bilingual synonymous technical terms, and achieve the performance of almost 98% precision and over 40% F-measure. Then,in order to improve recall, we introduce a semi-automatic framework, where we employ the strategy of selecting more than one seeds for each set of candidates bilingual synonymous term pairs.
By manually judging whether each pair of two seeds is synonymous or not, we achieve over 95%
precision and 50% recall.

2 Japanese-English Parallel Patent Documents
In the NTCIR-7 workshop, the Japanese-English patent translation task is organized (Fujii et al.,2008), where parallel patent documents and sentences are provided by the organizer. Those parallel patent documents are collected from the 10 years of unexamined Japanese patent applications
published by the Japanese Patent Office (JPO) and the 10 years patent grant data published by the
U.S. Patent & Trademark Office (USPTO) in 1993-2000. The numbers of documents are approximately 3,500,000 for Japanese and 1,300,000 for English. Because the USPTO documents consistof only patent that have been granted, the number of these documents is smaller than that of the
JPO documents. From these document sets, patent families are automatically extracted and thefields of “Background of the Invention” and “Detailed Description of the Preferred Embodiments”
are selected. This is because the text of those fields is usually translated on a sentence-by-sentencebasis. Then, the method of Utiyama and Isahara (2007) is applied to the text of those fields, andJapanese and English sentences are aligned.

3 Phrase Table of an SMT Model
As a toolkit of a phrase-based statistical machine translation model, we use Moses (Koehn et al.,2007) and apply it to the whole 1.8M parallel patent sentences. In Moses, first, word alignment ofparallel sentences are obtained by GIZA++ (Och and Ney, 2003) in both translation directions andthen the two alignments are symmetrised. Next, any phrase pair that is consistent with word alignment is collected into the phrase table and a phrase translation probability is assigned to each pair.
More specifically, we construct a phrase table in the direction of Japanese to English translation,
and another one in the opposite direction of English to Japanese translation. In the direction ofJapanese to English translation, we finally obtain 76M translation pairs with 33M unique Japanesephrases, i.e., 2.29 English translations per Japanese phrase on average, with Japanese to English
phrase translation probabilities P (pE | pJ ) of translating a Japanese phrase pJ into an English
phrase pE . For each Japanese phrase, those multiple translation candidates in the phrase table areranked in descending order of Japanese to English phrase translation probabilities. In the similarway, in the phrase table in the opposite direction of English to Japanese translation, for each English phrase, multiple Japanese translation candidates are ranked in descending order of English
to Japanese phrase translation probabilities.
Those two phrase tables are then referred to when identifying a bilingual technical term pair,given a parallel sentence pair SJ , SE  and a Japanese technical term tJ , or an English technicalterm tE . In the direction of Japanese to English, given a parallel sentence pair SJ , SE  containing
a Japanese technical term tJ , the Japanese to English phrase table is referred to when identifying abilingual technical term pair. From the Japanese to English phrase table, candidates of translatingtJ into English which are consistent with word alignment are collected. Then, those English

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translation candidates are matched against the English sentence SE of the parallel sentence pair,and those which are not found in SE are filtered out. Finally, among the remaining translation
candidates, t̂E with the largest translation probability P (tE | tJ ) is selected and the bilingual
technical term pair tJ , t̂E  is identified. The precision of identifying bilingual technical termpair here is 91.9%. Similarly, in the opposite direction of English to Japanese, given a parallel
sentence pair SJ , SE  containing an English technical term tE , the English to Japanese phrase
table is referred to when identifying a bilingual technical term pair.

Figure 1: Developing a Reference Set of Bilingual Synonymous Technical Terms

4 Developing a Reference Set of Bilingual Synonymous Technical Terms
Table 1: Number of Bilingual Technical Terms: Candidates and Reference of Synonyms
Candidatesof Synonyms
Reference ofSynonyms# of bilingual technical terms for the total 134 seeds CBP (sJ ) = 22,473

average per seed
167.7

sJ





SBP (sJE ) = 1,680


12.5

sJE

The following describes the procedure of developing a reference set of bilingual synonymous
technical terms from the whole 1.8M parallel patent sentences and the Japanese to English / English to Japanese phrase tables. Figure 1 illustrates the whole procedure.
1. First, a initial Japanese noun phrase t0J is randomly selected from the Japanese part of the
1.8M parallel patent sentences.
2. Then, to the initial Japanese noun phrase t0J , the following “Iteration: Generating Candidates Bilingual Synonymous Term Pairs” is applied, where the iteration is repeated steps oftranslation generation from the 1.8M parallel patent sentences and the Japanese to English/ English to Japanese phrase tables1 . Next, the initial set CBP (t0J ) of candidate bilingual1

The number of iteration 6 here is based on our preliminary evaluation, and is decided so that most synonymous
bilingual technical terms are generated from the initial Japanese phrase t0J , while the number of candidates otherthan true synonyms is minimized. Throughout those steps, we simply avoid duplicate generation of terms.

198synonymous term pairs is generated as in the left half of Figure 1.
Iteration: Generating Candidates of Bilingual Synonymous Term Pairs
1st step Given the input Japanese term tJ , collect all the parallel sentence pairs which contain tJ from the 1.8M parallel patent sentences. Next, from each parallel sentence pair,tJ is translated into English according to the procedure in the previous section, referringto the Japanese to English phrase table. Then, all the bilingual term pairs tJ , tiE  arecollected into the initial set CBP (tJ ) of candidates bilingual synonymous term pairs2 .
2nd step Similarly, for each English term tE in CBP (tJ ), collect all the parallel sentencepairs which contain tE from the 1.8M parallel patent sentences, and translate tE into
Japanese, referring to the English to Japanese phrase table. Then, all the bilingual termpairs tiJ , tE  are added to CBP (tJ ).
3rd step Similarly, for each Japanese term tJ in CBP (tJ ), collect all the parallel sentencepairs which contain tJ from the 1.8M parallel patent sentences, and translate tJ into
English, referring to the Japanese to English phrase table. Then, all the bilingual termpairs tJ , tiE  are added to CBP (tJ ).
4th step Repeat the procedure of the “2nd step”.
5th step Repeat the procedure of the “3rd step”.
6th step Repeat the procedure of the “2nd step”.
After the candidate generation iteration, we restrict the set CBP (t0J ) as having more than orequal to 10 members (i.e., | CBP (t0J ) |≥ 10). In the evaluation of this paper, out of 4,000
randomly selected initial Japanese noun phrases and corresponding initial sets CBP (t0J ),about 350 sets satisfy the lower bound of the number of members.
3. Next, out of the members of the initial set CBP (t0J ) of candidates bilingual synonymous
term pairs for the initial Japanese noun phrase t0J , we select the seed bilingual term pairsJE = sJ , sE  as below:
First, in order to distinguish technical terms and general terms and to select bilingual technical term pairs as seeds, we assume the candidates of seeds to satisfy at least one of thefollowing requirements:(a) The co-occurring frequency of the bilingual term pair in the 1.8M parallel patent sentences is less than 500.
(b) The character length of the Japanese term is more than two when it contains kanji (Chinese characters) or hiragana (Japanese characters). The Japanese term consists of morethan one morpheme when all of its characters are katakana (Japanese characters forforeign words).
(c) The English term consists of more than one word.
Then, we manually examine the bilingual term pair with the largest co-occurring frequency
in the 1.8M parallel patent sentences. If the one with the largest co-occurring frequency
is appropriate as a pair of technical terms, we select it as seed. Otherwise, we manually
examine all the members of the initial set CBP (t0J ) and select the most appropriate pair asseed. If the initial set CBP (t0J ) does not include any pair of bilingual technical terms, we
discard the set CBP (t0J ) at this step.
In the evaluation of this paper, out of all the initial sets CBP (t0J ), for about 29% of theinitial sets, we keep the bilingual term pair with the largest co-occurring frequency as seed,
for about 14% of them, we manually select as seed the pair other than the one with the largest
2

Throughout the steps from the “1st” to the “6th”, we only keep bilingual term pairs which satisfy the lower bound 6
as well as the upper bound 800 of the co-occurring frequency in the 1.8M parallel patent sentences.

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co-occurring frequency, and for the remaining 57%, we discard the initial sets CBP (t0J ). It
took about 5.5 minutes on average to manually examine all the members of each initial set
CBP (t0J ).
4. To the Japanese technical term sJ of the seed bilingual technical term pair sJE = sJ , sE ,“Iteration: Generating Candidates Bilingual Synonymous Term Pairs” is applied. As the result of this iteration, the set CBP (sJ ) of candidates of bilingual synonymous technical termpairs is generated as in the right half of Figure 1. Here, we again restrict the set CBP (sJ )as having more than or equal to 10 members (i.e., | CBP (sJ ) |≥ 10). In the evaluation ofthis paper, about 90% of the sets CBP (sJ ) satisfy the lower bound of the number of members. Finally, we have 134 seed bilingual technical term pairs, where the number of bilingual
technical terms in total and their average are shown in Table 1.
5. Finally, for each seed bilingual technical term pair sJE = sJ , sE , we manually divide theset CBP (sJ ) of candidates of bilingual synonymous technical term pairs into SBP (sJE ),those of which are synonymous with sJE , and the remaining N SBP (sJE ). As in Table 1,
the number of bilingual technical terms included in SBP (sJE ) in total for all of the 134
seed bilingual technical term pairs is 1,680, which amounts to 12.5 per seed on average.

5 Automatic Identification of Bilingual Synonymous Technical Terms by Machine
Learning
In this section, we apply the SVMs to the task of identifying bilingual synonymous technicalterms, which we originally proposed in Liang et al. (2011).

5.1 The Procedure
First, let CBP be the union of the sets CBP (sJ ) of candidates of bilingual synonymous technicalterm pairs for all of the 134 seed bilingual technical term pairs. In the training and testing of theclassifier for identifying bilingual synonymous technical terms, we first divide the set of 134 seedbilingual technical term pairs into 10 subsets. Here, for each i-th subset (i = 1, . . . , 10), weconstruct the union CBPi of the sets CBP (sJ ) of candidates of bilingual synonymous technicalterm pairs, where CBP1 , . . . , CBP10 are 10 disjoint subsets3 of CBP .
As a tool for learning SVMs, we use TinySVM (http://chasen.org/˜taku/
software/TinySVM/). As the kernel function, we use the polynomial (2nd order) kernel.
In the testing of a SVMs classifier, we regard the distance from the separating hyperplane to eachtest instance as a confidence measure, and return test instances satisfying confidence measuresover a certain lower bound only as positive samples (i.e., synonymous with the seed). In the training of SVMs, we use 8 subsets out of the whole 10 subsets CBP1 , . . . , CBP10 . Then, we tune thelower bound of the confidence measure with one of the remaining two subsets (henceforth named
as the development set). With this subset, we also tune the parameter of TinySVM for trade-off
between training error and margin. Finally, we test the trained classifier against another one of
the remaining two subsets (henceforth named as the evaluation set). We repeat this procedure oftraining / tuning / testing 10 times, and average the 10 results of test performance.

5.2 Features
Table 2 lists all the features used for training and testing of SVMs for identifying bilingual synonymous technical terms. Features are roughly divided into two types: those of the first type
f1 , . . . , f6 simply represent various characteristics of the input bilingual technical term tJ , tE ,
3Here, we divide the set of 134 seed bilingual technical term pairs into 10 subsets so that the numbers of positive (i.e.,synonymous with the seed) / negative (i.e., not synonymous with the seed) samples in each CBPi (i = 1, . . . , 10)
are comparative among the 10 subsets.

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Table 2: Features for Identifying Bilingual Synonymous Technical Terms by Machine Learningclass

featuresforbilingualtechnical
terms
tJ , tE 

featuref1 :frequency

f2 :rank of the Japanese
term

f3 :

rank of the English term

f4 :number of Japanesecharactersnumber of English words
number of times generating translation by applying the phrase tables

f5 :
f6 :

f7 :

featuresfortherelation ofbilingualtechnical
terms
tJ , tE 
and
the
seed
sJ , sE 

f8 :
f9 :

f10 :

f11 :

f12 :

f13 :
f14 :
f15 :

f16 :

identity of Japanese
terms
identity of English terms
edit distance similarityof monolingual terms
character bigram similarity of monolingual
terms
rate of identical morphemes (for Japanese) /words (for English)
subsumption relation ofstrings / variants relation of surface forms (forJapanese terms )identical stem (for English terms)
hyphen / space (for English terms)
compositional translation with an existingbilingual lexicontranslation by the phrase
tabledefinition( where X denotes J or E, and s J , sE  denotes
the seed bilingual technical term pair )log of the frequency of t J , tE  within the whole parallel
patent sentences
given tE , log of the rank of t J with respect to the descending order of the conditional translation probability
P(tJ | tE )given t J , log of the rank of t E with respect to the descending order of the conditional translation probabilityP(tE | tJ )number of characters in t J
number of words in t E
the number of times repeating the procedure of generating translation by applying the phrase tables until generating tE or tJ from sJ , as in sJ → · · · → tJ → tE , or, sJ→ · · · → tE → tJ
returns 1 when t J = sJ
returns 1 when t E = sE
ED(tX ,sX )f9 (tX , sX ) = 1 − max(|t(where ED is the
X |,|sX |)edit distance of tX and sX , and | t | denotes the numberof characters of t.)
X )∩bigram(sX )|f10 (tX , sX ) = |bigram(t(wheremax(|tX |,|sX |)+1
bigram(t) is the set of character bigrams of the term t.)|const(tX )∩const(sX )|f11 (tX , sX ) = max(|const(t(whereX )|,|const(sX )|)const(t) is the set of morphemes (for Japanese) / words(for English) in the term t.)
returns 1 when the difference of t J and sJ is only in theirsuffixes, or only whether or not having the prolonged
sound “ー”, or only in their hiragana parts.

returns 1 when the numbers of constituent words of t E
and sE are the same, and their corresponding constituents
have the same stem.
returns 1 when the difference of t E and sE is only
whether having hyphen or space.
returns 1 when s J can be compositionally generated bytranslating constituents of t E with an existing bilingual
lexicon, or, s E can be compositionally generated bytranslating constituents of t J with an existing bilingual
lexicon (Tonoike et al., 2006).
returns 1 when s J can be generated by translating t E withthe phrase table, or, s E can be generated by translating t Jwith the phrase table.

while those of the second type f7 , . . . , f16 represent relation of the input bilingual technical term
tJ , tE  and the seed bilingual technical term pair sJE = sJ , sE .

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Among the features of the first type are the frequency (f1 ), ranks of terms with respect tothe conditional translation probabilities (f2 and f3 ), length of terms (f4 and f5 ), and the numberof times repeating the procedure of generating translation with the phrase tables until generating
input terms tJ and tE from the Japanese seed term sJ (f6 ).
Among the features of the second type are identity of monolingual terms (f7 and f8 ), edit
distance of monolingual terms (f9 ), character bigram similarity of monolingual terms (f10 ), rateof identical morphemes / words (f11 ), string subsumption and variants for Japanese (f12 ), identicalstems for English (f13 ), hyphen / space of English terms (f14 ), compositional translation with anexisting bilingual lexicon4 (f15 ), and translation by the phrase tables (f16 ).

5.3 Evaluation Results
Table 3: Evaluation Results of Automatic Identification of Bilingual Synonymous Technical Terms (%)

Baseline (tJ and sJ are identical, or, t E and sE are identical.)
SVM
Maximum Precision
Maximum F-measure

Precision
67.0
97.5
73.5

Recall
54.3
28.7
68.1

F-measure
60.8
43.9
70.5

Table 4: Examples of Improvement in Automatic Identification of Bilingual Synonymous Technical Terms
by SVM(a) Correctly Judging as “Synonym” only by SVM
seed sJ , sE 
tJ , tE 
ReferenceBaseline
SVM (Maximum Precision)
 ホールド回路,
 保持回路,synonym not synonym
synonymhold circuit
holding circuit

seed sJ , sE  転写器,
transfer unit(b) Correctly Judging as “Not Synonym” only by SVM
tJ , tE 
ReferenceBaseline SVM (Maximum Precision)
 搬送ユニット,
not synonym synonym
not synonymtransfer unit

Table 3 shows the evaluation results for a baseline as well as for SVMs. As the baseline, we
simply judge the input bilingual term pair tJ , tE  as synonymous with the seed bilingual technicalterm pair sJE = sJ , sE  when tJ and sJ are identical, or, tE and sE are identical. When training/ testing a SVMs classifier, we tune the lower bound of the confidence measure of the distancefrom the separating hyperplane in two ways: i.e., for maximizing precision and for maximizing Fmeasure. When maximizing precision, we achieve almost 98% precision where F-measure is over
40%. When maximizing F-measure, we achieve over 70% F-measure with over 73% precisionand over 68% recall.
Table 4 also show examples of improving the baseline by SVMs.
Table 4 (a) shows the case of correctly judging as “synonym” only by the proposed method.
Here, the baseline judges as “not synonym”, since neither tJ and sJ nor tE and sE are identical.
With the proposed method, on the other hand, f13 returns 1 since “holding” and “hold” have thesame stem. Also, f16 returns 1 since, by the phrase tables, “ホールド回路” can be generated bytranslating “holding circuit”, and “保持回路” can be generated by translating “hold circuit”.
Table 4 (b) shows the case of correctly judging as “not synonym” only by the proposed method.
Here, the baseline judges as “synonym”, since tE and sE are identical. With the proposed method,
4

As the existing Japanese-English bilingual lexicon, Eijiro (http://www.eijiro.jp/, Ver.79, with 1.6M translation pairs, is used.

202

Figure 2: The Procedure of Semi-Automatic Transitive Identification of Bilingual Technical Termson the other hand, both edit distance similarity f9 and character bigram similarity f10 return 0
for the Japanese terms “転写器” and “搬送ユニット”. Also, f15 returns 0 since, by compositional translation with an existing bilingual lexicon, “転写器” cannot be generated by translating“transfer unit”, nor “搬送ユニット” cannot be generated by translating “transfer unit”.

6 Semi-Automatic Approach to Transitive Identification of Bilingual Synonymous
Technical Terms
Evaluation results of the previous section is satisfactory in terms of precision of identifying bilingual synonymous technical terms. However, its recall is relatively low, which needs to be improved. In this section, we allow semi-automatic approach to the task of identifying bilingualsynonymous technical terms. In this approach, we assume that the SVM classifier trained by theprocedure of section 5.1 is tuned so that it can achieve high precision against relatively easier instances, while it judges relatively harder instances as not synonymous, resulting in relatively low
recall. Based on this assumption, we design a manual process which is responsible for examining
whether pairs of bilingual technical terms judged by the SVM classifier as not synonymous are
not actually synonymous.

6.1 The Procedure
The detailed procedure of the semi-automatic approach is presented in this section.
1st step Suppose that we are given the SVM classifier trained by the procedure of section 5.1.
Then, for a set CBP (sJ ) of candidates of bilingual synonymous technical terms, apply
the SVM classifier to every pair uJE = uJ , uE  and vJE = vJ , vE  of the members of
CBP (sJ ).
2nd step Next, for each member uJE = uJ , uE  of CBP (sJ ), collect uJE itself and othermember vJE = vJ , vE  of CBP (sJ ) judged as synonymous with uJE by SVM into a set
X(uJE ) (Figure 2 (a))5 .

X(uJE ) = vJE = vJ , vE  (∈ CBP (sJ ))  vJE = uJE , or, vJE is judged assynonymous with uJE by SVM.
5Here, if both u1J E and u2J E are judged as synonymous with vJ E , but u1J E and u2J E are judged as not synonymous,
we simply include both u1J E and u2J E in X(vJ E ).

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3rd step For each bilingual technical term pair uJE = uJ , uE  (∈ CBP (sJ )), if |X(uJE )| > 1
holds6 , then manually examine whether uJE is synonymous with the seed bilingual technicalterm pair sJE , i.e., whether uJE ∈ SBP (sJE ) holds (Figure 2 (b)). If so, then collect themembers of X(uJE ) into XX(sJE ) (Figure 2 (c)).
Finally, the set XX(sJE ) can be regarded as the final output of the procedure for semiautomatic transitive identification of bilingual technical terms that are judged as synonymouswith the seed bilingual technical term pair sJE , and can be denoted as the following formula:XX(sJE ) =X(uJE )
uJ E ∈SBP (sJ E )6.2 Evaluation Results
In the evaluation, for each of the 134 seed bilingual technical term pairs, we evaluate the precision,
recall, and F-measure of the set XX(sJE ). As in the case of the procedure of section 5.1, withthe development set, we tune the lower bound of the confidence measure as well as the parameterof TinySVM for trade-off between training error and margin, so that we can control the precisionof the set XX(sJE ) as over 80%, 85%, 90%, and 95%. Evaluation results against the evaluation
set are shown in Table 5. Here, we achieve over 95% precision with more than 50% recall, andover 90% precision with almost 70% recall. As can be clearly seen from these results, by simply
transitively merging the results of identifying bilingual synonymous technical terms by SVM, we
can improve the recall of bilingual synonym identification task7 .
Table 5: Evaluation Results of Transitive Identification of Bilingual Synonymous Technical Terms (%)
Requirement for Precision against the Development Set
> 80%
> 85%
> 90%
> 95%

Precision
81.3
86.9
91.3
95.2

Recall
89.9
80.9
69.1
53.1

F-measure
85.1
83.4
78.2
67.9

7 Related Works
Among related works on acquiring bilingual lexicon from text, Itagaki et al. (2007) focused on
automatic validation of translation pairs available in the phrase table learned by a statistical machine translation model, where their study differs with this paper in that Itagaki et al. (2007) did
not study the issue of synonymous bilingual technical terms. Tsunakawa and Tsujii (2008) ismostly related to our study, in that they also proposed to apply machine learning technique tothe task of identifying synonymous bilingual technical terms and that the features of machinelearning studied in Tsunakawa and Tsujii (2008) are closely related those studied in this paper.
However, Tsunakawa and Tsujii (2008) studied the issue of identifying synonymous bilingual
technical terms only within manually compiled bilingual technical term lexicon and thus are quite
limited in its applicability. Our study in this paper, on the other hand, is quite advantageous in that
we start from parallel patent documents which continue to be published every year and then, that
we can generate candidates of synonymous bilingual technical terms automatically.
6

This condition means that at least one technical term pair vJ E is judged as synonymous with uJ E by SVM. Otherwise, we skip the process of manually examining the pair vJ E .
7
In
this evaluation,
we simply measure the average number of the members uJ E of SBP (sJ E ) for each of which¬¬¬¬¬X(uJ E )¬ = 1 holds.

This number represents that, for how many members of SBP (sJ E ), we can actually skip
examining whether uJ E is synonymous with the seed bilingual technical term pair sJ E . Out of the average 12.5
members per seed, in Table 5, the numbers are 0.9 for “> 80%”, 1.4 for “> 85%”, 2.2 for “> 90%”, and 4.0 for “>95%”, respectively.

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Our study in this paper is also different from previous works on identifying synonyms basedon bilingual and monolingual resources (e.g. Lin and Zhao (2003)) in that we learn synonymous
bilingual technical terms from phrase tables of a phrase-based statistical machine translation modeltrained with very large parallel sentences.
In Liang et al. (2011), we proposed the framework of applying machine learning technique tothe task of identifying bilingual synonymous technical terms in the process of acquiring technicalterm translation equivalent pairs from parallel patent documents. The major drawback of theframework of Liang et al. (2011) is in its low recall when preferring precision as over 90%. The
framework proposed in Liang et al. (2011) is also employed in the first half of this paper, where
the major contribution of this paper is in showing that the approach of manually merging synonym
candidate sets identified by SVM in the first half of this paper is quite effective in improving low
recall reported in Liang et al. (2011).

8 Conclusion
In the task of acquiring technical term translation equivalent pairs, this paper studied the issueof identifying synonymous translation equivalent pairs. We applied the SVMs to this task andachieved the performance of almost 98% precision and over 40% F-measure. Then, in orderto improve recall, we simply introduced a semi-automatic framework, where we employed thestrategy of selecting more than one seeds for each set of candidates bilingual synonymous termpairs. By manually judging whether each pair of two seeds is synonymous or not, we achieved
over 95% precision and 50% recall. We are planning to incorporate the results of judgment by
SVM when judging whether each pair of two seeds is synonymous or not.

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