Identification of Bibliographic Information
Written in both Japanese and English
Yuko Taniguchi

Hidetsugu Nanba

Toshiba Solutions CorporationTaniguchi.Yuko@toshiba-sol.co.jp

Hiroshima City University
nanba@hiroshima-cu.ac.jp

Abstract. We have studied the automatic construction of a multilingualcitation index by collecting Postscript and PDF files from the Internet.
We propose a method to identify duplicate bibliographic information
written in both Japanese and English, which will be an indispensable
module for the construction of a multilingual citation index. To confirm
the effectiveness of our method, we conducted an examination and foundthat our method obtained a precision of 94.8% and a recall of 25.6%.
Key words: translation of technical terms, multilingual citation index,
identification of interlingual bibliographic information.

1Introduction

We have studied the automatic construction of a multilingual citation index bycollecting Postscript and PDF files from the Internet [2], and in this paper, we
propose a method that can identify duplicate bibliographic information written
in both Japanese and English, which will be an indispensable module for theconstruction of a multilingual citation index.
There are several related citation indices, such as Google Scholar(http://www.google.com/schhp), CiteSeer (http://citeseer.ist.psu.edu/), andPRESRI (http://www.presri.com) [2], which include Postscript and PDF files
located on the World Wide Web (WWW). These indices are constructed in twostages: (1) the extraction and (2) the integration of bibliographic information.
In the extraction stage, bibliographic information and a list of references areextracted from each paper (Postscript and PDF), and in the integration stage,
the extracted bibliographic information is gathered and integrated. A key technique in this stage is to identify any duplicate bibliographic information, andmethods of identifying intralingual bibliographic information have already been
proposed and implemented in previous systems. However, interlingual identification of bibliographic information is required to construct a multilingual citationindex. The following text is an example of the same bibliographic information
written in Japanese and English.
[Japanese title]
山田太郎, サポートベクトルマシンを用いた自動要約, Vol. 30, No. 18, pp.
10-21, 2000.
[English title]
Taro Yamada, (2000) “Automatic Summarization based on Support Vector
Machines,” Journal of Natural Language Processing, 30 (18), pages 10-21.
(in Japanese)2

Yuko Taniguchi, Hidetsugu Nanba

As shown in this example, “in Japanese” is written after the bibliographic
information in a list of references when a Japanese language paper is cited in an
English language paper. In such cases, the corresponding Japanese bibliographic
information should be detected. However, traditional systems cannot identify
this bibliographic information and therefore we focused on the identification of
duplicate interlingual bibliographic information.

2Identification of Interlingual Bibliographic Information

2.1 Procedure for the Identification of Bibliographic Information
To identify intralingual bibliographic information, traditional systems extract atitle, author(s) name(s), year of publication, and the number of pages from alist of references, and then compare each field. In our task, we also extracted
the same fields from each bibliographic information source, and compared eachfield. Here, the fields “number of pages” and “publication year” were directlycomparable, while a machine translation technique was required to compare thefields “title” and “author(s) name(s)”. We can use several resources, such as
dictionaries, for Japanese morphological analysis and for kana-kanji conversion
to translate an author’s name. However, the “title” field was difficult to translate using general machine translation systems, because in general, a title isa large noun phrase containing some technical terms. Therefore, we identified
interlingual bibliographic information using the following four steps.
1. Analyse the structure of a title based on some cue phrases,
2. Extract a series of nouns as technical terms from the titles,
3. Translate extracted technical terms based on a statistical translator for technical terms [1],4. Identify interlingual bibliographic information based on the structure of thetitles and the results of the translation of technical terms.
In the next section, we will elaborate on the first step.
2.2 Analysing the Structure of Titles
We analysed the structure of titles using cue phrases. The following text shows
two examples of the results of our analysis of bibliographic information, shownin Section 1, using our method.
[Japanese title]<METHOD> サポートベクトルマシン </METHOD> を用いた <HEAD>
自動要約 </HEAD>[English title]<HEAD>Automatic Summarization</HEAD> based on <METHOD>SupportVector Machines</METHOD>
In the Japanese title, the “METHOD” tag is assigned to “サポートベクトルマシン” (Support Vector Machines), because the cue phrase, “を用いた” (basedon) appears just after it. In the English title, the “METHOD” tag is assigned
to “Support Vector Machines,” because the cue phrase “based on” appears justbefore it. The “HEAD” tag is assigned to the last noun phrase in the JapaneseIdentification of Bibliographic Information

3title and to the first noun phrase in the English title. Finally, we translated
each tagged technical term using a statistical translator [1], and then comparedthem for each tag. We prepared 31 cue phrases in English and 165 cue phrases
in Japanese to analyse the structure of the titles. Using these cue phrases, we
manually made rules to assign 10 types of tag to each word in a title. We show
a part of these tags and cue phrases in Table 1.
Table 1. A part of tags and cue phrases
Tag
Cue phrases (English) Cue phrases (Japanese)
METHOD
based on, using, by を用いた, に基づいた, によるRESTRICTin, on, ofにおける, に関する, のGOAL
towards, forに向けて, のためのCONJ
and, orと, や, 及び3Experiments

We conducted the following experiment to confirm the effectiveness of our method.
3.1 Data
We used the data set used for the Cross-lingual Information Retrieval (CLIR)
Tasks in the first and second NTCIR Workshops (NTCIR-1 and NTCIR-2).
This data set contains about 330,000 bibliographic information items, and XMLstyle tags were assigned to the title, author(s) name(s), year of publication,and abstract, all of which were written in Japanese and English. We randomly
selected 750 pairs and used them for our task.
3.2Alternatives

We identified bibliographic information using the following four methods.
– Method 1. Use a translation of technical terms in the titles.
– Method 2. Use Method 1 and a translation of the author(s) name(s).
– Method 3. Use Method 2 and a publication year.
– Method 4. Use Method 3 and any tag information.
As a baseline method, we identified bibliographic information using a translation of the author(s) name(s) and year of publication. To translate the author(s)name(s), we used ipadic, which contains 33,000 Japanese personal names andtheir pronunciations.
3.3 Evaluation
We evaluated the four methods and the baseline method using precision andrecall, as defined in the following equations.
P recision =Recall =T he number of titles that the system could detect correctly
T he number of titles that the system detected(1)

T he number of titles that the system could detect correctly
T he number of titles that should be detected

(2)

4

Yuko Taniguchi, Hidetsugu Nanba
Table 2. A comparison of our methods and the baseline method.
Baseline method
Method 1
Method 2
Method 3
Method 4

Precision (%) Recall (%)
1.7
93.3
3.4
29.4
17.3
29.4
91.9
29.4
94.8
25.6

3.4 Results and Discussion
Table 2 shows our experimental results.
As can be seen from the Table 2, both “translation of author(s) name(s)”(Method 2) and “year of publication” (Method 3) are useful for improving theprecision. The “tag information” (Method 4) was also useful, because the taginformation improved the value of the precision by 2.9%. However, the tag information decreased the recall value by 3.8%.
The main reason for the low recall using our methods is that English titlesare not always correct translations of Japanese titles. We show a typical example
of a pair of English and Japanese titles that our method could not identify.
[English] Study of the dialogue model for an intelligent support system ofgroup learning[Japanese] 知的グループ学習支援システム のための’ 対話モデルの研究
From the English title, our system extracted three terms “dialogue model”,“intelligent support system”, and “group learning”, and correctly translated
these into “対話モデル (dialogue model)”, “知的学習支援システム (intelligentsupport system)”, and “グループ学習 (group learning)”, respectively. However,
two terms, “intelligent support system” and “group learning”, in the English
title were integrated into a single noun phrase, “知的グループ学習支援システム(intelligent group learning support system)” in the Japanese title. These cases
caused the low recall value. Analysing the structure of each term in the titles isrequired to resolve this problem. This study aims to identify elementary terms
in a technical term, and to analyse the dependency relationship between them,and although there have been many studies in the fields of terminology study or
natural language processing, research in this area is still very difficult.

4Conclusions

We have proposed a method to identify interlingual bibliographic information
based on analysing the structure of titles. In experiments, our method obtained
a precision of 94.8% and a recall value of 25.6%, confirming the effectiveness ofour method.

References
1. Fujii, A., and Ishikawa, T. Cross-Language Information Retrieval for Technical Documents. In Proceedings of the Joint ACL SIGDAT Conference on EMNLP and VLC,
29-37, (1999)
2. Nanba, H., Abekawa, T., Okumura, M., and Saito, S. Bilingual PRESRI: Integrationof Multiple Research Paper Databases. In Proceedings of RIAO 2004, 195-211 (2004)