One of the activities of the National Institute of
Japanese Literature (NIJL) has been the designing,
building, managing, and maintaining of mainframe-based information systems for Japanese classical literature, made available to researchers in
Japan and abroad. At present, we provide three
catalogue databases: Catalogue of Microfilms of
Manuscripts and Books on Japanese Classical Literature; Catalogue of Manuscripts and Books on
Japanese Classical Literature; and Bibliography of
Research Papers on Japanese Classical Literature.
We have also compiled some full-text databases.
When we began constructing full-text databases,
SGML was not yet popular in Japan, and unfortunately there were no SGML applications that
could handle the Japanese language. For these
reasons, we established our own markup system
resembling SGML in its basic conception . We
refer to this system as the “KOKIN rules” (from
KOKubungaku [Japanese literature] INformation). “Kokin” is also the title of one of the foremost collections of Japanese classical poems.
The KOKIN rules were designed to be easy to
understand for researchers of Japanese classical
literature and easy for them to use. However, as
the markup system is unique and independent,
there are no tools available to parse and check
KOKIN files. Although SGML was originally a
document markup language for publishers, it is
now widely accepted as an encoding scheme for
transmitting text data irrespective of the platform.
Given SGML’s international importance and the
fact that it has become popular in Japan, we decided that we should convert our text data to SGML
markup to enable wider dissemination . We
thus began a new project aimed at constructing a
full-text database using SGML .
Section 2 of this paper describes the KOKIN Rules, and section 3 covers our project to convert
KOKIN text to SGML markup. Finally, there is a
summary of some problems regarding the markup
of classical Japanese texts.
2. Text markup using the KOKIN rules
This section describes the KOKIN rules: the Tag
rule, the Flag rule, and the Value-added rule.
2.1 Tag rule
Text is composed of various kinds of logical elements (titles, chapters, etc.). Tags are identifiers
that mark up logical elements of text, and the
resulting “markup” reflects how a researcher sees
and analyzes the text. For example, we see a line
as a basic text structure and call it a “logical
record”. Furthermore, we treat a series of logical
records as a “logical record set”. Several continuous lines that constitute a poem are an example of
the logical record set called “poem”. Thus, the
KOKIN rules can describe the hierarchical structure of text.
The KOKIN rules were designed to provide guidelines for researchers who want to transcribe
primary Japanese classical texts using a computer.
There is thus a similarity with the TEI . Our
intention was that further text analysis and tag
definitions should be performed by researchers
themselves. Thus the electronic texts we transcribed define only the basic structure such as titles,
pages, and lines. The Tag rule shows how to define
the basic structure of a text using the “JapaneseYenMark” followed by a letter of the alphabet (i.e.,
‘T’ for title, ‘P’ for page, and ‘G’ to mark the insert
position for a graphic element). The following is
the syntax of the Tag rule:
<Logical Record> ::= <Tag Begin><Tag><Tag End> |
<Tag Begin> ::= "JapaneseYenMark"
<Tag End> ::= "JapaneseStarMark"
<Tag> ::= <Tag Symbol> | <Tag Symbol>
<Data> ::= <Line> | <Original Data>|
<Repeating Symbol><Original Data>
<Line> ::= <Original Data> | <Number>
<Repeating Symbol> ::= ’;’
<Tag Symbol> ::= .........
<Tag Attribute> ::= .........
<Original Data> ::= .........
2.2 Flag rule
We believe most Japanese classical texts have a
two-dimensional nature – that is, a text is composed of the primary material (titles, body, etc.) and
supplementary material (annotations, side notes,
etc.). The Flag rule was introduced to define regions of such supplementary material.
The Flag Rule employs the delimiter ‘/’ at the start
and end of a word in the primary material to which
supplementary material is attached. The supplementary material is enclosed by ‘(‘ and ’)’. The
following is the syntax of the Flag rule:
<Original Data> ::= <Flag Begin><Data Element>
<Data Element> ::= <Paragraph>|<Phrase>|<Word>|
<Space> ::= <Space Between Words>|
<Space Between Characters>
<Flag Begin> ::= ’/’
<Flag End> ::= ’/’
<Space Flag> ::= ’/’
<Supplement> ::= <Right Supplement>|<Left
<Right Supplement> ::= <Right Supplement Begin>
<Left Supplement> ::= <Left Supplement Begin>
<Bi-Supplement> ::= <Supplement Begin><Supplement
Element> ’|’ <Supplement Element>
<Supplement Element> ::= <Single Supplement>|<Double
<Single Supplement> ::= <Supplement>
<Double Supplement> ::= <Supplement><Supplement
<Right Supplement Begin> ::= ’(’
<Left Supplement Begin> ::= "(|"
<Supplement End> ::= ’)’
<Supplement Separator> ::= ’#’
<Supplement> ::= <String>|<String><String
<String Separator> ::= ’=’
2.3 Value-added rule
One of the main purposes of computer-based
textual studies is to create a vocabulary index.
However, as there are no spaces between words of
Japanese text, it is not easy to identify individual
words. Thus researchers must perform this task
before lexical studies can begin. Unfortunately,
different researchers employ different criteria
when identifying individual words. Complexity is
added by various compound words that appeared
over the centuries, resulting in differences from
work to work, genre to genre, and period to period.
This makes Japanese lexical analysis very difficult.
As mentioned above, we believe that tasks such as
these, requiring considerable professional skills,
should be performed by the researchers themselves, but our Value-added rule provides assistance
for such markup. Using this rule, a researcher can
separate a sentence into space-delimited words,
adding attributions to any word – such as the
phonetic values of Chinese ideographs. These attributions are enclosed by ‘[‘ and ’]’. The following is the syntax of the Value-added rule:
<String> ::= <Value Added Begin><Word>
<Value Added End><Value Added>
<Value Added> ::= <Value Begin><Values><Value End>
<Values> ::= <Value 1>|<Value 2>|<Supplement
Value>|<Value 1><Binding Symbol>
<Value 1> ::= <Phonetic Representation of a
Chinese Ideograph><Attribution 2
Attribution End>|<Repeating Symbol>
<Value 2> ::= <Attribution 1 Begin><Variation>
<Attribution End><Attribution 2 Begin>
’,’ <Information><Attribution End>|
<Repeating Symbol><Element 2>
<Value Supplement> ::= Not Use
<Variation> ::= "Part of Speech" | "Name" | "Location" |
<Value Added Begin> ::= ’ ’
<Value Added End> ::= ’ ’
<Value Begin> ::= ’(’
<Value End> ::= ’)’
<Attribution 1 Begin>::= ’[’
<Attribution 2 Begin>::= "[,"
<Attribution End> ::= ’]’
<Binding Symbol> ::= ’!’
<Repeating Symbol> ::= ’;’
3. Case study
For our case study, we chose to apply SGML
markup to an anthology of short stories dating
from the Edo period (1600-1868). This text has a
complex structure, involving annotations, editorial corrections, phonetic representations, and so on.
The original text had been transcribed from a
printed edition by a colleague, then marked up
using the aforesaid KOKIN rules. This experience
had convinced us of the descriptive ability of our
markup method, especially for Japanese classical
literature. However, the KOKIN rules are essentially abstract, and there are no tools – such as
parsers and editors – available for researchers.
Starting in 1993, we applied SGML markup to this
same anthology in order to see if SGML was a
practical alternative for Japanese classical texts.
There were three stages in this case study:
1) creation of a DTD for the anthology;
2) conversion of the original KOKIN text data to
3) construction of a full-text database system based on string-search tools.
The tools used were MARK-IT (Sema Software
Technology) for data conversion and structure
analysis, and OPEN-TEXT (Open Text Co.) for
3.1 DTD creation
KOKIN-to-SGML data conversion involved replacing the “JapaneseYenMark” and following
tag to the START-TAG (<) and END-TAG (>) of
SGML. However, text marked up according to the
KOKIN rules involves some ambiguities – for
example, the open-parenthesis symbol is used,
according to the Flag rule, for <Supplement Begin>, but according to the Value-added rule as
<Value Begin>. Fortunately, differentiation is
easy: examples of the former appear after the ‘/’
of <Flag End>, while the latter appears after the ‘’
of <Value Added End>. Nevertheless, this is context dependent and thus unacceptable under
SGML (context-free grammar class).
To resolve these ambiguities, we analyzed the
syntax of the KOKIN rules using the E-R model
to check precisely the relation between each tag
and flag defined in the rules. The DTD we created
was checked using a DTD compiler.
3.2 Text data conversion
As the original data had been input and marked up
manually, there were many transcription errors
and structural inconsistencies that had to be eliminated. Next, we constructed pre-processing algorithms based on the E-R model to convert ambiguous structures to unambiguous ones. Programs
were then compiled using these algorithms. Finally, an SGML parser (MARK-IT) was employed to
convert pre-processed data to fully SGML-compliant data. MARK-IT checked the input data and
added any tags that had been omitted.
3.3 Full-text searching
In contrast to the relatively simple process of data
encoding, text searching posed several problems.
First, we constructed a full-text database using a
relational database system. Though a relational
database has standard query tools based on an
elegant mathematical model (i.e., SQL, QBE), this
type of database imposes fairly strict restrictions
on data structure. An object-oriented database is
more suitable for complex data structures, but it
has neither standard search methods nor any equivalent of SQL.
Nevertheless, as our goal could be described as the
ability to “search for a string in a desired region in
data,” we could construct a full-text database system based on string searching. In the study of
Japanese classical literature, searching for strings
of interest is a common task. Consequently, we
concluded that a full-text database system based
on a string-searching system would be effective
Fast string-searching machines and software are
available, and some of these can handle Japanese
SGML data. We selected OPEN-TEXT as the
basis for our full-text database system.
4. Some problems
Applying SGML to works of Japanese classical
literature involves some problems.
4.1 Electronic dictionary
The first problem involves the compound words
mentioned above. If we require electronic texts for
such intensive study as lexical analysis, spaces
must be inserted between words. There are many
ways of accomplishing this, such as string matching with dictionaries and the application of artificial intelligence methods. However, none are
satisfactory, owing perhaps to the fact that there
are no substantial electronic corpora or electronic
dictionaries for Japanese classical literature. Of
course, the reason why there are no such corpora
or dictionaries is that there are relatively few electronic Japanese classical texts available. In other
words, we have a chicken-and-egg situation.
4.2 Complex annotations
The second problem involves describing attribute
information such as annotations. Many scholars of
Japanese classical literature hope that a markup
system can encode any original text feature that is
important for their research. However, the structure of classical text is very complicated. For
example, a string may have two separate annotations; furthermore, the main string and both annotations may be split over two or more lines as
Second Annotation for First Line...
First Annotation for First Line.......
TEXT LINE on First Line.... Annotated String on First Line....
.......Second Annotation for Second Line
First Annotation for Second Line
...Annotated String on Second Line TEXT LINE on Second Line.....
KOKIN Rule describes this structure as follows.
TEXT LINE on First Line..../Annotated String on
First Line = Annotated String on Second
Line/(First Annotation for First Line = First Annotation for Second Line # Second Annotation for
First Line = Second Annotation for Second Line)/
TEXT LINE on Second Line ................
Here, ‘=’ indicates where a string is split over two
lines, parenthesis symbols ‘(‘ and ’)’ indicate the
starting and ending positions of the annotations,
and ‘#’ separates the first and second annotation.
The same structure can be described by SGML as
TEXT LINE on First Line.........................................
<SuppElement FG="ON">Annotated String on First
<SuppElement FG="ON">Annotated String on Second
<FirstSupp>First Annotation for First Line</FirstSupp>
<FirstSupp>First Annotation for Second Line</FirstSupp>
<SecondSupp>Second Annotation for First Line
<SecondSupp>Second Annotation for Second Line
TEXT LINE on Second Line...........................................
Here, the tag <SuppElement> denotes an annotated string region. The attribution “FG" indicates
whether or not the annotated string is split over
two lines; thus, FG="ON" means the annotated
string continues over two lines. <DoubleSupp>
after <Supplement> means there are two annotations for the string enclosed by <SuppElement>.
<FirstSupp> denotes a first annotation, and <SecondSupp> means a second annotation. Repeated
occurrences of <FirstSupp> or <SecondSupp>
mean that the annotation is split into two or more
4.3 Non-nested structure
Not infrequently there are missing pages in our
original materials. First, we considered treating
these missing pages as layout information, using
empty tags to indicate them. However, some researchers believe missing pages are essential information. In some cases, they can recover the
missing material by referring to other texts. The
problem is that if we use ordinary tags to indicate
a region of missing pages, these tags will often
overlap with another region, as in the following
...........<MissingPage>... Recovered Text .....
Here, if some material is recovered by a researcher, then the <MissingPage> region crosses
over into the <Chap> region, thus violating the
SGML nesting rule.
4.3 Kanji characters
The most serious problem may be that posed by
kanji characters (Chinese ideographs). Some researchers believe that they need over 50 thousand
kanji characters to describe all Japanese texts.
However, the Japan Industrial Standard (JIS) lists
only 12,546 characters, and of these only 6,355 are
standard for computer use. Thus, some users, companies and computer centers make their own additional character sets; these characters are called
“external characters” since they fall outside the
range of standard characters.
Our institute has made about two thousand external characters and more than ten thousand fonts.
Printing and displaying these external characters
is only possible within the institute, and no elegant
solution is yet available to enable data exchange
with other sites.
External characters are often used for historical
terms, technical terms, and proper nouns such as
names. However, there are often alternative Chinese ideographs with the same meaning. Making
use of this fact, we employ external characters
within the center to publish catalogues that require
the correct kanji, but we use alternatives from the
standard character set for users on the network
who are satisfied as long as they understand the
meaning of the original. The same electronic data
is used, whether the output is local or on-line, as
the external characters are paired with their JIS
Here, “%JIS” must be set to “INCLUDE” and
“%External” to “IGNORE” if we want to compile
data for network database services, and “%JIS” to
“IGNORE” and “%External” to “INCLUDE” for
As a test project, we have produced a full-text
database and string-search system conforming to
SGML. This shows that electronic Japanese classical texts can clearly be constructed on a common
SGML model. We are now developing guidelines
for transcribing primary Japanese classical texts
using SGML. There are still some problems that
have to be addressed, but we are hoping to learn
from other projects, such as the Wittgenstein Archives at the University of Bergen (WAB), which
involves complex formatting issues.
In future, we will determine whether our system
can perform satisfactorily under real conditions.
In addition, we propose a multimedia database in
which texts and images are linked.
 Hisashi Yasunaga: Data Description Rule and
Full-text Database for Japanese Classical Literature, Joint International Conference
ALLC-ACH Conference Abstracts, pp.234-
 Shoichiro Hara and Hisashi Yasunaga: On the
Full-text Database of Japanese Classical Literature, Joint International Conference ALLCACH Conference Abstracts, pp.61-63, 1993.
 Shoichiro Hara and Hisashi Yasunaga: On the
Text Based Database Systems for Public Service: Joint International Conference ALLCACH Conference Abstracts, pp.43-45, 1995.
 L. Burnard et. al.: Guidelines for Electronic
Text Encoding and Interchange (TEI P3),
ALLC, 1-3, 1994.
If this content appears in violation of your intellectual property rights, or you see errors or omissions, please reach out to Scott B. Weingart to discuss removing or amending the materials.
Hosted at University of Bergen
June 25, 1996 - June 29, 1996
147 works by 190 authors indexed
Scott Weingart has print abstract book that needs to be scanned; certain abstracts also available on dh-abstracts github page. (https://github.com/ADHO/dh-abstracts/tree/master/data)
Conference website: https://web.archive.org/web/19990224202037/www.hd.uib.no/allc-ach96.html