Hello, everybody, welcome to our latest research on HeidelGram. Today on 16th
century British grammar writing. The 16th century marks for us the beginning
of English grammar writing and here's the quotation from Terttu Nevalainen
supporting this. And also one, a contemporary quotation also referring
to parts of speech, which is the typical Latin category, Latin grammar category.
For us. We'd like to see English grammar books as indicators of changing and stable
language norms. That's why we are looking at them also for the 16th century.
It's situated within HeidelGram project, which compiles and analyzes
a corpus of 16th to 19th century English historical grammars and to look at new
linguistic and network analytical approaches to see actually what
they are doing, how they refer to each other. And here is some previous work
we have done, for example, on grammarians reference categories. Now, the aims
of this paper is we'd like to see whether the categories we already established
for references for the 19th century also can work for the 16th century data.
We'd also like to develop new categories for types of authors based on grounded
theory. So it's really work in progress. We'd like to trace onomastic references
in the 16th century grammars using citation networks and with that
a very important aim for us is to automatize and reflect the process of creating those
networks and improve concordance extractions. You will see that in a minute
how we did it. We like to define grammar by using a rather standard approach.
Here, MacCarthy. That is the rules and conventions of everyday language.
This is something the term grammar really implies and also the book as a grammar.
But we like to add Cameron's Verbal Hygiene concept of cleaning up language
and make its structure, its use, conform more closely to the ideals of beauty,
truth, efficiency, logic and correctness. Because you'll see that in the 16th
century even we have both rules and exactly those kind of normative, sometimes just
descriptive statements. For this research, we have selected are
data based on the criteria established for the 19th century subcorpus. However,
we were restricted by the sparsity of available data, which is why we have these
five shown grammars as our dataset today. Also, this research accounts for any
particularities of this historical data, such as the regular prints due
to the mechanics of 16th century printing presses and also references made
in margins as can be seen in the image here.
I'm going to run you through the process of extracting these graphs or networks.
So we started off with scans of grammars and these are usually PDF and then these
were taken into a raw corpus using OCR as well as manually typing them up due
to the way the data looks like. What then followed was a process of manual
annotation that led to an annotated corpus. And we focused here for this
project on annotating references to figures that are referenced in these books
out of the annotated corpus, which was then also added to our HeidelGram database,
we extracted a list of search terms and these search terms are all of the
figures that are being referenced. And this list was then also extended with
existing research. We use that then to normalize our corpus, which is a casefolded,
version, in which all of the names, named references, are normalized so that
we can look for them more easily. At the same time, we use this list alongside
our annotated corpus, using a grounded theory approach to come up with categories
describing these authors. Now in the next step, we use the normalized corpus as well
as our list of search terms to generate a list of concordances that were then coded
by two independent raters or coders using the existing categories for references
from the 19th century - using a grounded theory approach - as well as the newly
created author categories. In both cases, we got very respectable Interrater
Reliability Scores, so we are relatively comfortable that these categories actually
work quite well, which led then to a set of classified classified concordance,
which we then used to generate our graphs, which you will see in a second. Take
a minute, stop this video, have a look at this slide because it is rather
complicated and then keep going. This slide now shows the established
categories of references that were extracted within the 19th century
research. Please do take a minute and take a look at these examples to get a good
feel for them. On this slide, we have the categories of referenced authors
which are novel to this research. They have been extracted based on Grounded Theory
and we see unexpected references, such as political figures. So grammarians
do make reference not only to other grammar authors. Please do have a look
at these categories as well. So now we would like to show you our
visually pleasing "flowers," which is actually a network
of our 16th century grammar corpus. So this is sort of an overview of all the patterns
that we found in our dataset, which is very rich, as you can see. So the square
nodes that you can see in the center here are the actual grammar texts themselves
so Sherry, Mulcaster, and the others whereas the round notes refer
to the figures being mentioned in the text, and these are color coded. So,
for example, the blue nodes refer to religious figures and the edges
are the reference categories themselves, which are also color coded. So,
for example, we have the pink edges that refer to quotations. So also the size
of the nodes were adjusted in order to reflect salience within the dataset. So,
for example, the larger round notes indicate that these figures
are referenced more frequently, whereas the thicker edges actually show that
a specific type of reference is being made more. The five Gramma's
definitely behaves quite differently. And so, for instance, Meurier is actually not
depicted in this network because we could not find any relevant references.
And you see here, for example, that there is this interconnectedness between Sherry
and Mulcaster and Coote, whereas Bullokar, on the other hand, although we do find
a reference to a religious figure, Saint Michael, it's not actually being
referenced in the other texts as well. So. I also now want to go into the ego-graphs
a little bit just to show you some futher examples. So for instance, we see
the interconnectedness between Sherry and Mulcaster. If we look at Caesar, Quintilian
and Cicero, who are quite prominent in the texts. So there is this reference Lutsen
and this tradition of rhetoric and rhetoricians. So for example, Cicero,
we can see here he is color coded as an interdisciplinary scholar and we see that
there are quite a lot of quotations that occur in Sherry. And also there
are quotations in reference to Cicero that occur in Mulcaster as well. So, yeah,
there are also references to specific works that were found in the margins,
as we previously mentioned. So these are mostly for religious figures and these
are also incorporated into this overall network.
So now we also want to show you some of our results. So here, we can see
the distribution of the different categories within the corpus, for example,
Sherry, there are quite a lot of quotations and mentions. Mentions
are also salient in Coote. However, with some of the other types of references,
such as acknowledgments, we don't see very many examples. And then for the author types
we can also see, for instance, the political figures are particularly common, as well as
the interdisciplinary scholars. We see Coote referenced a lot of religious figures,
whereas Bullokar and Meurier don't actually make a lot of references to these specific authors.
Next steps! Now, in addition to finding out why this is the case, why these patterns
emerge, we will extend our analysis to the 17th and 18th century and therefore close
the gap. We'll further analyze verbal hygiene patterns. And or very dear to us
is the data and software being made available. And the description
of our process should be made available in line with trustworthy research and FAIR
data principles, hence reproducibility. Check the latest on our project website.
Thank you for your attention!