Today on this gorgeous summer, we will be coming up.
We will witness a short introduction.
This is a bit of a rural format for the participants,
because they have to reach their talk to see the short,
but it's important for us because we get to do more
based on the experience of the United States.
We'll follow up with the format that will describe
the amount of hours of digital technology
in such an instant.
A short question to the individual authors
followed by the general discussion at the end,
so that we can understand that.
Our first speaker is a short paper
by Joanna Schandinger and Gretina Stauber-Autumn,
in terms of the opportunity to talk about the distinction
that's really important to us.
Yes, thank you.
So we have 10 minutes to run through a lot of material,
and I just heard that I actually can't use my notes to speak.
So this will turn into sort of a mumbled jungle
of disconnected sentences,
and I hope you will still get some meaning out of that.
So what I'm presenting here is the follow-up
of some research work that Karina van Dann-Osskam
and I did already in 2007
and published in literary and linguistic computing.
At that time, we had a way of analyzing
the Dutch, Middle Dutch, Arthurian novel,
Vallewein, the Romans of Gawain in English,
which originated around 1260, is of course Middle Dutch first.
It's around 12,000 lines of length.
We only have one manuscript left of that particular novel.
Now, the particular problem with this novel is,
well, interesting problem to us in any way,
is that it self-claims in the prologue
that it has been written by Penning and a second author, Vostard,
and there has been some qualitative research
into where actually the second author, Vostard,
takes over from the first author, Penning.
Traditionally, this hand-off is placed somewhere
between 7,872 and 7,930.
Also, complicating matters a little
is that this Longish first is also created by two copyists.
So the one extant manuscript that we have
actually has two hands of copyists.
And of course, because of identifying the handwriting,
the actual takeover point for the second copyist
is at first 5,784.
Now, the method for the problem is,
we're just using the standard Burris Delta approach,
but that usually takes into account
sort of an average over a large corpus of text
that you can compare to the text that you do not know.
Now, in this case, we just don't have
that much male Dutch text, and moreover,
we don't have much digitized male Dutch text,
and even more important, we don't have text
compared to the same period, the same genre, and known author.
So this is really a sort of close-off mysterious case
where we can't compare with external matter.
So what we did was adapt Burris Delta a little,
basically, probably maybe even abuse it
in the statistical sense by using the full text
and the full length of the text itself
as the average base of high-frequency words,
and then sliding a window through the complete text
line by line to see if we can get a Delta score
for that particular window.
And in that case, you end up with a graph like this,
but this is the graph for the highest-frequency words,
so the ranking, the 1 to 50 ranking highest-frequency words,
and that gives you a particular nice drop-off
at the expected point where we would see
that the second author takes over.
But complicating the matter is that if we look
in another subset of the high-frequency spectrum,
you see that there's a different graph,
and actually the peak that we see is probably identifiable
with the changeover, the handover of the copiers.
So we had good results, but you also had a lot of questions
actually still going on after these results.
We still want to know if that one text
and many windows sliding through is a valid approach.
I mean, if we would be able to compare this method
to other text, do the results live up to our expectations?
Secondly, Burrows uses a target text.
We also use the target text, which was basically
just taking an undisputed part in the second half
of the Romans, which we know must be by Vostard.
But I've, interpreting our result,
I've been come to question if that is actually a good base,
a good target, or is it just biasing our results?
We also wanted to know, because we saw different results
in different subsets of the high-frequency spectrum,
we wanted to know if those results are stable
or that they vary and what that means.
And in the end, whether that say something about
is this copious distinction, is this authorship distinction,
or both, or even maybe nothing in the worst case.
So what we needed was software that would actually allow us
to make a move forward and go from a sort of a 2D graph
sliding through the text to a kind of a 3D representation
where we would have multiple windows sliding through the text,
each taking a delta comparison for a specific subset in the spectrum.
Of course, because we want to also provide this software
for others, it needed to be easy to use, easy to test
or more text, et cetera, et cetera, et cetera.
So I'm going to introduce delta 3D beta.
It's at this moment fully available at a GitHub location.
It's not running as a web service yet.
I hope to accomplish that in August,
but this involves some security issues, et cetera,
and installing services.
Basically what it does, it takes your text as a JSON input
and it starts analyzing the text given specifications
for the window, the steps of the window through the text, et cetera, et cetera.
So this is going to be the part where everything gets difficult.
Maybe it works.
That doesn't fit, does it?
Does anybody know how to make this fit, actually?
There we go.
Unfortunately, this doesn't show all that.
Anyway, so first of all, we wanted to test whether a graph,
a sliding graph of a normal, undisputed,
Dutch modern novel would yield no specific interesting anomalies.
So what I'm now doing is actually looking at a turning circle,
which means that I'm uploading that text and now the analysis is running.
So it's sliding the windows through the text.
At some point it will give us...
Sorry?
No, no, no, no, no, this is running from my laptop.
So what you see here is a representation.
Basically it's a lot of those graphs you saw before,
but then represented in the various spectrums,
subset of the high-frequency word spectrum.
Now this is a bit of a mumble jumble, of course,
but we have various representations of the same graph
so that you can choose which one to look at.
And this is basically telling us that nothing very specific happens in this text.
It's a representation of an internal stylistic consistency of the text.
You don't see any specific high or low depths out of the range of the whole text.
That's important to remember.
If I move on to the actual text and I upload and process that,
it's that you will see if we have a little time yet.
It's actually 200% quicker than my last software,
but it still takes some time because it does all these millions of calculations.
So there you are.
So this is the graph for the actual valor line, Romans of Gawain.
Probably this is the most telltale.
Basically you see again that for various parts of the text nothing much happens,
but indeed again we see our well-known peaks and troughs
at sort of the same location.
But there's also problems with this graph.
I'm returning to my PowerPoint now.
Let's see.
First of all, we chose to not use target text in this case,
a base text in this case anymore because if you run that,
you see what happens is this.
What you're simply seeing is that at that point the text compares the most to itself,
which is completely logical if you're comparing a small fragment of the text through the full text.
Actually, we don't need that bias because if we don't use the base of the bias,
we get the very same results that we would expect from our hypothesis.
Only there's a slight problem that the peak here that we used to identify with the copious distinction,
I'm not sure that that is actually something that we can hold to.
It will need far more analysis and far more looking into the actual word use
around those peaks to give a definite answer.
But given these new results, our previous hypothesis is that any graph
for a while and I would look like this, but actually I'm wondering if it shouldn't look like this.
This is ongoing work.
I wanted to present the fact that we have this tool that anybody will be able to use online
to compare comparisons like this and getting delta graphs like this for individual text,
which we need to see the validity of this tool.
Still, our results live up to our expectations.
This is our interpretation of these results until now.
Actually, this is again, we think, statistical proof for actually Volstar taking over,
another author taking over at the place where we would expect it in the text.
There's lots to be done for this software as well.
Particularly, we want to be able to slide through, give you the possibility
to click on a particular part of the graph and see what the words are doing exactly there.
This will be a feature in the fall, I hope.
That's it for a very quick overview.
Ready to take any questions and comments, of course.