My name is Yoshimi Iwata, at Tushin University.
Today, I'll talk about tracing the history of no-tech boosts by mathematical methods.
First, I'll talk about the history of no- and no-pray rights in my introduction.
Second, I'll show you the background of my research.
Third, I'll briefly explain my no-study methodology.
Finally, I'll give you my conclusions.
My purpose is to identify no-relics.
In this research, I used no-tech astro data
and calculated distances between each taxa by normalized compression distance.
I want to create such a result.
Now, I'll begin with a brief introduction of no- and no-research.
No- is a traditional Japanese stage art with a 600-year history.
At the end of the 14th century, no- was formalized by Kanami and his son, Zeyami.
Zeyami especially is said to be the greatest playwright in the Japanese no-shiata.
240 songs are in existence and are still performed today.
There are many well-known no-pray rights, including Zeyami,
but it's very difficult to find corresponding relationships between plays and their authors.
There is a branch of no-study which focuses on this problem.
In this field, researchers attempt to identify anonymous authors, mainly in the 14th and 15th centuries.
One of the difficulties in this field is the research materials are remarkably rare,
and there are different copies of each no-relics due to school or time period variation.
Therefore, no-researchers are always struggling with these problems
as they attempt to improve their own methodologies.
I believe Culture and Information Science, or CIS, can save these researchers.
Currently, traditional attitudes about data sharing and technical reasons prevent them from sharing data.
To support them, currently, I'm developing a versatile database schema related to no studies
which can provide information about songs, dances, and lyrics.
Here, I'll focus on the methods or operations which will be employed to create the database.
Now let's move on to the main subject.
I'll explain my data processing and analytical methods.
The purpose of this research is to support current researchers in their efforts to identify anonymous authors.
I chose one of the most popular no plays, titled Tadanori.
This slide shows a summary of the 11 versions of Tadanori used in this study.
These versions were translated into data files respectively.
The side column on the table shows the original file texts in bytes.
This is a flow chart which shows the methodology of lyrics analysis.
The users, say researchers, query the database and then the system returns the results of preliminary analysis automatically
after the user chooses an adequate analytical method in accordance with their goals.
Finally, the user drove a complete map for further studies.
Practical analysis directly connected to the individual researcher's interpretations are based on the preliminary analysis.
Now, I'm focused on the flow between database query and preliminary analysis.
I'll talk about one of the approaches, normalized compression distance, or NCD.
NCD is a parameter-free similarity matrix theoretically based on the Kolomogorov complexity,
which can be applied not only to text files but also to various kinds of digital files.
This is NCD formula. Here, C of x, y denotes the compressed size of the concatenation of the file object x and the file object y.
C of x denotes the compressed size of x and C of y denotes the compressed size of y.
I use Nori-Rik text data. In my experimental study, NCD was calculated by Hyakkawang on NCD-calculating software developed by Yu Fujimoto.
This software creates text vector of NCD matrix. This software currently supports general zip, gzip, and bzip algorithms, and I use general zip compression.
I use the NavalNet to visualize the genealogy of Nori-Rik, though the original advocates of NCD recommend the correct method for clustering.
This is the result of the NavalNet derived from the NCD matrix. NCD is very easy to use.
Because the researcher can directly use the data querying database, but when using classical methods, data coding is required.
Data coding of Japanese literature is difficult because there is no space between each word and their different characters are used kanji, hiragana, and katakana,
and Nori-Rik were written in obsolete old Japanese. It's a really tough process even for Japanese researchers.
In contrast to these classical methods, NCD requires no inconvenient coding processes which free the researchers from having to go through this process.
Here, I'll compare the results of the NCD NavalNet method and two other classical methods, the gawa distance neighbor joining method and the maximum parsimony method, both of which require data coding.
For these classical methods, I prepared a dataset which I had coded manually.
Though it was really tough for me. In the networks shown on this slide, the data prefixes represent different known schools.
In all three results, the KP group show here in red and the KZ group, card and blue are separated. This means the text can be categorized according to schools.
As to HS 1979, all three networks show it closer to the KZ group.
The general consensus of the NO community is the HS group and the KZ group belong to the same group, while KP belongs to another group which was not included in this study.
Therefore, these three results support these general theories. Although NCD tends to be criticized because of the difficulty involved in verifying the NCD results, the approach in my research was fully satisfied and can be applied to preliminary analysis.
In conclusion, NCD can be a powerful tool for preliminary analysis because this method needs no preliminary processing, which requires knowledge of ancient Japanese grammar or Chinese and Japanese characters.
The fact the NCD data conform to the general theories in the NO community is encouraging. In addition, NO relics have been handed down from generation to generation by no actors up until the present with few changes, which makes them compatible with NCD.
I'm going to continue to design the database by reflecting the results of this research. Thank you very much for your attention.