It's strange.
So it's my great pleasure to be here
and have an opportunity to tell you about the project
I'm working on still now.
The aim is to locate words in a text from French Renaissance
time, also called early French, perhaps early English
from the 16th to the 18th century.
You could tell me, OK, Google it, and you'll find that word.
But that is a good idea if you're
working with texts from the 21th century,
but not from texts with that times,
because there are no spelling rules.
There are no orthographic meaning at the time.
And if you want, for example, to locate the exact quote
from that saying from Montaigne, quesège,
you will have to operate the gap between that way of spelling
and that way of spelling.
The texts I'm working on are available on the Bibliothèque
Virtual Humanist.
It's a website in two.
And this library is linked to other projects
like Gallica or Europeana.
Even if a lot of you heard about this period,
from this period, just enjoyed the opportunity
to share nice and educative pictures,
let us have a talk through this labyrinth of chart
in that case.
So everybody knows about castles, flying machines,
fantastic paintings.
Or from this first printing place,
it's a kind of pre-de-age tool to develop the humanities.
And they are at that time exploring
the cosmos and the inside, putting in it
to cabine de curiosity, that the French were to say,
pre-de-age data warehouse.
So all these people, Macquaville, Thomas More,
Rablée, Montaigne, are writing books.
And books are looking that day at the time.
I can read it as French, human being.
Cetissie en l'Ivoire de bonne fois, le quatre,
il t'avertite de l'entre, que je me suis proposé,
nous le fan, que de mystique et privy, and so on.
But the extraction of information
during the digitization process is not so easy.
That's why you have to analyze the book pages
to detect areas with different kind of informations.
They are extracted, categorized, and indexed.
And they put it into databases.
Like that's one portrait gallery,
but you can't have a search for lion motifs.
Or printing woods, for example.
And we want to do this because we want to allow people
to go direct to the information they are looking for.
A virtual library can just give access to books.
They need to give access to content.
For the textual part, you first need to authorize
or transcribe the text.
And you can then work on that part
with linguistic tools.
And VeriaLog is one of that tool,
overbriting the gap between old and new spelling.
So you've seen some pages written in that French.
If you know French, you can see the problem I want to solve.
But let me give you some more examples.
For example, en, un, one, can be written
V-N-G or O-N-G. Un coté, that's vie, savoir, je seis,
and so on.
Sometimes it's really strange.
I couldn't imagine a French person reading that word
and guessing it's callier.
It's really not difficult. So now you know that problem exists.
You want to get sure if it's really often the case or not.
So let's have a look in the text.
This is a text from Montaigne.
The red words are the word written in a different way.
It's a lot, but most of them are quite like French
from that time.
But when you're looking in the text from Rablais,
then it's awful.
I mean, not awful.
Enjoying.
But as you see, this is really difficult problem to solve.
You perhaps want to build a list of words to understand.
Oh, no.
To see what happened exactly for one word.
And therefore, témoignage, you find that right.
Souvren, that's wrong.
And you can, if you have a very long list of such words,
then you can be surprised because it's not only that
Y can be written in E, I, but E as A, AU as O, US as U,
and so on, C for C. You can find CH, Q, CQ, QUS, SSTE,
I don't know, and much more.
So when you do the combinatory in the world
from all that spellings, it's very amazing.
You could perhaps want to give up,
but a human being can read it.
So you just have to find a way to solve the problem
so that your machine can read it.
There's several ways to do that.
You can try with an NLP program.
So we tried to adapt an engram tagger first,
but the proofreading was very difficult.
And then we developed an annotation tool.
But the high-quality text annotation
is still a very slow process, and it's
a time-consuming one.
So we want to achieve such an annotation,
but also want to use a text if and if they are not annotated.
So we distinguish both applications,
slower editorial process, and in a search and design process,
find all character strings equivalent to the one
you're looking at.
So then we decided to expand the character string
into a list of words, or possible words,
using a set of rules based on linguistic information,
history, spelling, morphology, history, and so on.
So you can find that way for spell is the same way to speak.
It could be happened now.
You can recognize here some morphological process or here
some flexion process.
Then I built a typology and write the substitution tools
to generate all the possible equivalent.
So now you have got the rules.
Sorry for that.
And you can go to your task.
You want to find some words, for example, that words.
In this text we've seen before.
So it's a free available Java program I wrote.
We wrote to make that.
You can specify your encoding character string,
your rules files, copies, and target text,
and then go to the results.
It looks like that.
For each word which has been recognized,
you can have here a bevel explaining
the rule which was applied to detect that word.
And you have another possibility to see the results.
It is this kind of summarization from the different words
that are applied in the text.
So we thought it's nice like this
and then decided to make a real test and treat
the texties which are representative for the time.
Jean Fleury, La Réciance du la Due,
Ebréproportion de la Trozatic.
Can't translate it in English.
Ronsard on de Perrier.
And this representativeness is also for the words,
the list of words.
We describe all the well-known phenomena
and quite a lot of exceptions to the rules.
Then I have to say the results were surprising to me
because here you have the list of the words we decided to test.
And as you see, most of them are recognized.
They are really different right away.
This is not an easy task.
It's really the real life in that book.
If I believe my log file,
we identified 1,043 words over 1,154 words,
and that means a 99% match.
OK, it's surprising, but considering that the rules
that really often occur just once in that file,
because it was just one word to test it out,
it's a quite good result, so we are happy with that.
When you use that tool, you can also see a text, a whole text,
and find exactly, I've seen before,
the indications for the rules applied,
and you can find all the occurrences
of the different variants in the text
and come back to this variation here and so on.
For specific purposes, you can turn the rules fairly easy
because it's only a regular expression set of rules.
For example, if you want to create one or any one,
you just have to write that, for example, and to stop it,
you just have to put that sign on the beginning of the line.
And you can increase a form list of monsters
that often occurs, for example, in your corpus,
like DUSU, written that way, or NOS, written that way,
and you are working on such a case of text.
And so we are looking forward to integrate that
in search engine, XDF database, or in other linguistic tools,
like dissimilar log or analog, we've developed two.
And I would like to try to adapt it for other languages,
now in July, in German, and then in English,
and then I would like to do that for German.
So thank you very much for your attention.
Thank you.