Automatic Translation of Scholarly Terms into Patent Terms
Using Synonym Extraction Techniques
Hidetsugu Nanba1, Toshiyuki Takezawa1, Kiyoko Uchiyama2, Akiko Aizawa2
1

Hiroshima City University
National Institute of Informatics
1
3-4-1 Ozukahigashi, Asaminamiku, Hiroshima 731-3194 JAPAN2
2-1-2 Hitotsubashi, Chiyodaku, Tokyo 101-8430 JAPANE-mail: nanba@hiroshima-cu.ac.jp, takezawa@hiroshima-cu.ac.jp, kiyoko@nii.ac.jp, aizawa@nii.ac.jp
2

Abstract
Retrieving research papers and patents is important for any researcher assessing the scope of a field with high industrial
relevance. However, the terms used in patents are often more abstract or creative than those used in research papers,
because they are intended to widen the scope of claims. Therefore, a method is required for translating scholarly terms
into patent terms. In this paper, we propose six methods for translating scholarly terms into patent terms using two
synonym extraction methods: a statistical machine translation (SMT)-based method and a distributional similarity(DS)-based method. We conducted experiments to confirm the effectiveness of our method using the dataset of the
Patent Mining Task from the NTCIR-7 Workshop. The aim of the task was to classify Japanese language research
papers (pairs of titles and abstracts) using the IPC system at the subclass (third level), main group (fourth level), andsubgroup (the fifth and most detailed level). The results showed that an SMT-based method (SMT_ABST+IDF)
performed best at the subgroup level, whereas a DS-based method (DS+IDF) performed best at the subclass level.
Keywords: patent, research paper, distributional similarity, statistical machine translation

1.

Introduction

Retrieving research papers and patents is important for
any researcher assessing the scope of a field with highindustrial relevance. However, the terms used in patents
are often more abstract or creative than those used inresearch papers, because they are intended to widen thescope of claims. Therefore, a method for translating
scholarly terms into patent terms is required. In thispaper, we propose several methods for translating
scholarly terms into patent terms. Several techniques
have been proposed for obtaining paraphrases orsynonyms using a statistical machine translation
technique (SMT) (Callison-Burch et al., 2006; Quirk etal., 2004; Zhao et al., 2008; Zhou et al., 2006) and a
distributional similarity (DS) technique (Lee, 1999; Lin,
1998). We extended these techniques to the translation ofscholarly terms into patent terms, and we confirm theireffectiveness experimentally using the dataset from the
Patent Mining Task at the NTCIR-7 Workshop (Nanba etal., 2008).
The remainder of this paper is organized as follows.
Section 2 describes related work. Section 3 proposes our
method for translating scholarly terms into patent terms
using synonym extraction techniques. Section 4 presents
our experimental investigation of the effectiveness of our
method and a discussion of our results. Finally, we
provide our conclusions in Section 5.

2.

Related Work

There has been much research in the field of cross-genre
information retrieval, such as that presented in thetechnical survey task of the Patent Retrieval Task at theThird NII Test Collection for Information Retrieval(NTCIR) Workshop (Iwayama et al., 2002). This task
aimed to retrieve patents relevant to a given newspaper
article. Itoh et al. (2002) focused on Term Distillation,
where the distribution of the frequency word occurrence
was considered to be different in heterogeneous
databases.
Therefore, unimportant words are assigned high scores
when using TFIDF to weight words. Term Distillation isa technique for preventing the incorrect assignment ofweights by filtering out words.
However, some patent terms, such as magnetic recording
device, only appear in a patent database and TermDistillation cannot be applied in such cases.
To address this problem, Nanba et al. (2009) proposed amethod for paraphrasing scholarly terms into patentterms (e.g., paraphrasing floppy disc into magnetic
recording medium). This method focused on citation
relationships of the paraphrased terms among research
papers and patents. Generally, a research paper and apatent that have citation relationships tend to belong to
the same research field. Therefore, they paraphrased ascholarly term into a patent term using two steps: (1)retrieve research papers that contain a given scholarly
term in their titles; and (2) extract patent terms frompatents that have citation relations with the retrieved
papers. However, their approach assumed that a
researcher would sequentially input individual scholarly
terms into their translation system, and the decision of“which scholarly term should be translated” belonged tothe user. In general, many scholarly terms do not need to
be translated into patent terms in research papers. In thispaper, we evaluated our method by applying it to the taskof research paper classification with the International
Patent Classification (IPC) system, which is a global
standard hierarchical patent classification system. In thisstudy, the problem of “which scholarly term should betranslated” was decided automatically, which was
impossible with Nanba’s approach. In addition to thisproblem, Nanba’s method was also not easily applied toother languages.
In another study of cross-genre information access, thePatent Mining Task was conducted at the NTCIR-7 and-8 Workshops (Nanba et al., 2008, 2010). At theseworkshops, research papers were classified using the IPC
system. We therefore used this dataset to confirm theeffectiveness of our methods.
Another related research project is TREC Chemistry
Track1, initiated in 2009, which focuses on information
access in chemistry research papers and patents.

3.

Automatic Translation of Scholarly
Terms into Patent Terms Using
Synonym Extraction Techniques

We propose two translation methods: an SMT-based
method and a DS-based method. We describe thesemethods in the following subsections.

3.1 Statistical
MethodMachine

1.

2.

3.
4.
5.
6.

7.

3.2 Distributional Similarity-based Method
Lin (1998) and Lee (1999) proposed a method for
calculating the similarity between terms, which is known
as DS. The underlying assumption of their approach wasthat semantically similar words are used in similar
contexts. Therefore, the similarity between two terms
can be defined as the amount of information contained in
the commonality between the terms, divided by theamount of information in the contexts of the terms.
We automatically translate a scholarly term into a patent
https://wiki.ir-facility.org/index.php/TREC_Chemistry_Track

Analyze the dependency structures of all sentences
in a research paper database using the Japanese
dependency parser CaboCha2.
Extract noun-phrase-verb (with a postpositional
particle) pairs that have dependency relations fromthe dependency trees obtained in Step 1.
Count the frequencies of each noun-phrase-verb
pair.
Collect verbs and their frequencies for each nounphrase, creating indices for each noun phrase.
Create indices from a patent database in the same
way as a research paper database (Steps 1 to 4).
Calculate the similarities between two indices ofnoun phrases created in Steps 4 and 5 using the
SMART similarity measure (Salton, 1971).
Obtain a noun phrase with the highest similarity
score as a translation of a given scholarly term.

Figure 1 shows an example of two indices for a scholarly
term フロッピーディスク (floppy disc) and for a patentterm 磁気記録媒体 (magnetic recording device), both ofwhich are created in Steps 4 and 5, respectively.
scholarly term
フロッピーディスク(floppy disc)3 に _書 き 込 む(write_to)
2 に_収める(store_to)
2 に_取り込む(import_to)
1 は _読 み 取 る(read)

Translation-based

Several methods for obtaining paraphrases or synonyms
using statistical machine translation techniques havebeen proposed (Callison-Burch et al., 2006; Quirk et al.,
2004; Zhao et al., 2008; Zhou et al., 2006). If thetranslations of two expressions X and Y are the same
expression, then the expressions X and Y are considered
to be paraphrases. Based on this concept, Zhou et al.
(2006) automatically obtained paraphrases from atranslation model, which were created from pairs ofEnglish and Chinese sentences using the SMT technique.
These paraphrases were then used for evaluating
computer-produced summaries.
Here, if 高分解能 (high definition) and high resolution
are aligned using a translation model for research papers,
and 高解像度 (high resolution) and high resolution arealigned based on a patent model, we can translate thescholarly term 高分解能 (high definition) into 高解像度	 (high resolution) as the corresponding patent term.

1

term using DS in the following procedure.

patent term
磁気記録媒体	 (magnetic recording device)
4 に _書 き 込 む(write_to)
2 を_作る(create)2 は _読 み 取 る(read)
1 を_傷つける(break)Figure 1: Example of two indices for a scholarly term
and a patent term

4.

Experiments

To confirm the effectiveness of our method, we
conducted a series of experiments.

4.1 Experimental Conditions
Task
In this study, we used the dataset from the Patent Mining
Task at the NTCIR-7 Workshop (Nanba et al., 2008).
The aim of the Patent Mining Task was to classify
research papers (pairs of titles and abstracts) that were
written in Japanese using the IPC system at the subclass(third level), main group (fourth level), and subgroup(the fifth and most detailed) levels. The IPC system is a
global-standard hierarchical patent classification system.
One or more IPC codes are assigned manually to each2

http://code.google.com/p/cabocha/

patent to ensure effective patent retrieval. The sixth
edition of the IPC system contains more than 50,000
classes at the most detailed (subgroup) level. Table 1
shows an example of an IPC code “A01B 1/02.”
A
01
B
SectionClass
Subclass
Main group
Subgroup

1

/02

A
A01
A01B

Human necessities
Agriculture; forestry; hunting; etc.
Soil-working during agriculture orforestry; parts, details or accessories ofagricultural machines or implements, ingeneral.
A01B 1
Hand tools
A01B 1/02
Spades; shovels
Table 1: IPC code example for “A01B 1/02.”
Experimental Data and Evaluation Measures
We used the dataset for a formal run of the Japanesesubtask in the NTCIR-7 Patent Mining Task. IPC codes
were manually assigned to all 879 topics in the dataset(research papers). An example of a topic is shown in
Figure 2, where <TOPIC-ID> specifies the topic
identification
number,while<TITLE>
and<ABSTRACT> specify the title and abstract of theresearch paper to be classified.
<TOPIC><TOPIC-ID> 100 </TOPIC-ID><TITLE> DTMF (Dual Tone Multi-Frequency)transmission method for a mobile communication system</TITLE><ABSTRACT> A highly efficient speech-encoding
scheme called VSELP is adopted for Japanese digital
mobile communication systems. However, DTMP (Dual
Tone Multi-Frequency) signals are distorted by using
this encoding scheme. This paper presents a DTMF
signal transmission scheme. DTMF signals aretransmitted in the form of call control messages frommobile stations (MS) to the mobile control centre(MCC). In addition, necessary control capabilities in MS
and MCC are described. </ABSTRACT></TOPIC>
Figure 2. Example of a topic (translated into English).
On average, 2.3 IPC codes were manually assigned toeach topic. These correct data were then compared with alist of IPC codes using the classification system, and thesystem was evaluated in terms of MAP (mean average
precision).

Document Classification System
We used a k-NN-based document classification system.
This system introduced the Vector Space Model as aretrieval model, SMART for term weighting, and nounphrases (sequence of nouns), verbs, and adjectives as
index terms. The classification module produced a list of
IPC codes using the following procedure.
1.
2.
3.

Retrieve top 170 results using the patent retrieval
engine for a given research paper.
Extract IPC codes with query relevance scores foreach patent retrieved in step 1.
Rank IPC codes using the following equation.
n

Score(X) = Σ Relevance score of each patenti=1
that IPC code X was assigned
where X indicates the IPC code and n is the number ofpatents to which X was assigned in the top 170 retrieved
patents. Here, the value of 170 was determined in ourprevious work (Nanba, 2008).
Translation of Scholarly Terms Using Our Methods
We translated scholarly terms into patent terms using themethods described in Sections 3.1 and 3.2. We
considered that it was not necessary to translate
high-frequency scholarly terms such as “study” or“method” into patent terms, because their translation
might impair the classification accuracy. Therefore, we
tested the following two methods.
(1) Translating scholarly terms when their inverse
document frequency (IDF) scores were lower than athreshold value.
(2) Translating all scholarly terms.
Translation Models
We used Giza 3 and Moses 4 as translation tools. We
obtained translation models using a patent bilingual
corpus containing 1,800,000 pairs of sentences (Fujii etal., 2008) and a research paper bilingual corpus
containing 1,763,217 pairs of CiNii database 5 titles(TITLE model). In addition to these models, we also
obtained a model for research papers based on 600,000
pairs of sentences found in the CiNii database abstracts(ABST model).
Distributional Similarity
To calculate the DS, we used 600 million sentences froma Japanese patent database, which covered a total of 10
years. We also used 600,000 sentences from the CiNii
database abstracts.
Alternatives
We conducted tests using the following six methods anda baseline method.

3

http://code.google.com/p/giza-pp/
http://www.statmt.org/moses/
5
http://ci.nii.ac.jp/
4Our Methods
 SMT_ABST: Translate scholarly terms using
SMT-based method with an ABST model.
 SMT_ABST+IDF: Do not translate scholarly
terms if their IDF scores are lower than a thresholdvalue, when using the SMT_ABST method.
 SMT_TITLE: Translate scholarly terms using
SMT-based method with TITLE model.
 SMT_TITLE+IDF: Do not translate scholarly
terms if their IDF scores are lower than a thresholdvalue, when using the SMT_TITLE method.
 DS: Translate scholarly terms using the DS-based
method.
 DS+IDF: Do not translate scholarly terms if their
IDF scores are lower than a threshold value whenusing the DS method.
Baseline Method
 PAPER: Use scholarly terms without translation.	
 4.2 Results
The experimental results in Table 2 show that most of
our methods using the SMT technique (SMT_ABST,
SMT_ABST+IDF, SMT_TITLE, and SMT_TITLE+IDF)
were superior to the baseline method. However, theDS-based method (DS and DS+IDF) slightly improved
the baseline method at the subclass level.
Methods

Subgroup(5th level)

Main
Group
th

Subclass(3rd level)(4 level)

SMT_ABST
0.37860.5186
0.6691
SMT_ABST+IDF
0.38120.51970.6709
SMT_TITLE
0.37970.52080.6688
SMT_TITLE+IDF
0.37990.52040.6710DS
0.37930.51820.6717
DS+IDF
0.37940.51750.6744
PAPER (baseline)
0.37920.51850.6720
Table 2: MAP scores by our methods and a baseline
method.

4.3 Discussion
Effectiveness of IDF
The DS method incorrectly translated the general(high-frequency) scholarly term 提案手法 (our method)
into 残 留 黒 鉛 (residual black lead), whereas the
DS+IDF method did not translate this term, because the
IDF score was very low. Most of the methods that
included IDF scores performed better than those without
IDF scores. Based on this result, we suggest that it is notnecessary to translate general scholarly terms into patentterms.
The MAP score of the SMT_TITLE method wasapproximately the same as that of the SMT_TITLE+IDF
method. This was because general terms, such as 提案手
法 (our method) or 本研究 (this study), appear rarely inthe titles of research papers so these terms were nottranslated by the SMT_TITLE method.

Comparison of the Two Translation Methods
The experimental results showed that the DS methodtended to translate scholarly terms into related terms withthe same properties, whereas the SMT_ABST and
SMT_TITLE methods tended to translate them intosynonymous terms. For example, the scholarly term ワードプロセッサ (word processor) was translated into
ドローソフト (drawing software) using the DS method.
However, although “word processor” and “drawing
software” both refer to computer software, they are notsynonyms. Thus, the DS-based method was suitable for
retrieving a wider range of patents, whereas the
SMT-based method was more applicable for retrieving anarrower range. DS performed better at the subclasslevel, whereas SMT_ABST performed better at the
subgroup level.

5.

Conclusions

In this study, we proposed six methods for translating
scholarly terms into patent terms using synonym
extraction techniques. We conducted experiments toconfirm the effectiveness of our methods using thedataset of the Patent Mining Task at the NTCIR-7
Workshop. We found that the SMT-based method(SMT_ABST+IDF) performed best at the subgroup level,
whereas the DS-based method (DS+IDF) performed bestat the subclass level. We also found that most of themethods with IDF scores performed better than those
without IDF scores indicating that it is not necessary totranslate general scholarly terms into patent terms.

References
Callison-Burch, C., Koehn, P., and Osborne, M. (2006)
Improved Statistical Machine Translation Using
Paraphrases. In Proc. NAACL 2006, pp.17--24.
Fujii, A., Utiyama, M., Yamamoto, M. and Utsuro T.
(2008). Overview of the Patent Translation Task at the
NTCIR-7 Workshop. In Proc. 7th NTCIR Workshop
Meeting, pp.389--400.
Itoh, H., Mano, H., and Ogawa, Y. (2002). TermDistillation for Cross-DB Retrieval. In Proc. 3rd
NTCIR Workshop Meeting.
Iwayama, M., Fujii, A., Kando, and N., Takano, A.
(2002). Overview of Patent Retrieval Task at
NTCIR-3. In Proc. 3rd NTCIR Workshop Meeting.
Lee, L. (1999). Measures of Distributional Similarity. In
Proc. 37th ACL, pp.25--32.
Lin, D. (1998). Automatic Retrieval and Clustering of
Similar Words. In Proc. 36th ACL and 17th COLING,
pp.768--774.
Nanba, H., Fujii, A., Iwayama, M., and Hashimoto, T.
(2008). Overview of the Patent Mining Task at the
NTCIR-7 Workshop. In Proc. 7th NTCIR Workshop
Meeting, pp.325--332.
Nanba, H., Kamaya, H., Takezawa, T., Okumura, M.,
Shinmori, A., and Tanigawa, H. (2009). AutomaticTranslation of Scholarly Terms into Patent Terms. In
Proc. 2nd International CIKM Workshop on Patent
Information Retrieval (PaIR'09), pp.21--24.

Nanba, H., Fujii, A., Iwayama, M., and Hashimoto, T.
(2010). Overview of the Patent Mining Task at the
NTCIR-8 Workshop. In Proc. 8th NTCIR Workshop
Meeting, pp.293--302.
Nanba, H. (2008). Hiroshima City University at
NTCIR-7 Patent Mining Task. In Proc. 7th NTCIR
Workshop Meeting, pp.369--372.
Quirk, C., Brockett, C., and Dolan, W. (2004)
Monolingual Machine Translation for Paraphrase
Generation. In Proc. EMNLP 2004, pp.142--149.
Salton, G. (1971). The SMART Retrieval System –Experiments in Automatic Document Processing.
Prentice-Hall, Inc., Upper Saddle River, NJ.
Zhao, S., Niu, C., Zhou, M., Liu, T., and Li, S. (2008)
Combining Multiple Resources to Improve
SMT-based Paraphrasing Model. In Proc. ACL-HLT
2008, pp.1021--1029.
Zhou, L., Lin, C.-Y., Munteanu, D.S., and Hovy, E.
(2006). ParaEval: Using Paraphrases to Evaluate
Summaries Automatically. In Proc. HLT-NAACL 2006,
pp.447--454.