Good morning. How does that sound? Well, like Sebastian said, better quality than quantity.
And if you saw our title in the listing, I hope you're not disappointed that it's not
in daggard languages, but endangered languages. So I'll talk briefly about linguistic diversity
and Dogen languages, and we're using data from these languages from a group led by Jeffrey
Heath to look into the genealogical classification, speak about data processing, and then I'll
hand things over to Jelena for the statistical methods and output. I wasn't sure what kind
of crowd we'd have. So if you're a linguist, you can, you probably already know what I'm
going to say. But basically, this is an image of the world and the eight major languages
that are spoken in the world today. And what's astounding other than the geographical reach
is the fact that each of these languages is spoken by over 100 million people as a first
language. And if we look at this a little bit closer, what we see have a kind of dividing
line between a million and under. Roughly 95% of the world's population speaks roughly
5% of the languages. And if you flip that on its head, 95% of the linguistic diversity
in the world is spoken by only 5% of people. And so this brings us to endangered languages.
And we know we have around, we speculate we have around 7,000 languages spoken in the
world. And by the end of the century, we expect that about half of those will have disappeared.
Current estimates are that a language disappears every two weeks. And I would put in a plug
here if you want more information. It's endangered languages dot com. Google was so nice to provide
drinks last night, and they're also part of this effort to spread some of the, what's
going on. So Austin and Salabank, they point out a couple of different reasons for language
endangerment. Some are rarer, like natural catastrophes, unfortunately war. And the biggest
cause of language endangerment is actually language shift. And this is a prominent theme
in West Africa, where basically you get a shift from a generation to a different language.
And this kind of fits in with UNESCO's language vitality framework, where a language that's
kind of critically considered critically endangered is only spoken by grandparents. And as the
generations have come, there's been a shift from the native language to some other language
for, say, upward mobility purposes. So Dogon languages are spoken in Africa, in West Africa
and Mali. And this is what the geography looks like. So in the background, you can see an
escarpment, and it ranges between 100 and 300 meters. It's a sheer cliff. And it's quite large.
So think kind of like Irish rock. It's in the middle, and there's really nothing else around it.
And here you can see that there is a village on the plains at the bottom. There are villages kind
of right on the cliff or actually in the cliff. And then there are also villages on top of the
escarpment and what we call the plateau. So there's a nice dispersed geographical landscape
for these languages to have kind of evolved over the years. What's interesting is up until about
1990, Dogon was considered in some reference books as just one language. And we know that now there
are at least 20 different Dogon languages. The current Dogon linguistic situation is completely
unclear. We don't know how many languages there are yet. We don't know how the languages are
related to each other. And we don't know how the languages as a language family belong in the
greater language families of Africa. In fact, last week, Jeffrey Heath's group discovered,
a new language in Dogon country. And so the goal of our paper is basically to use some of the
computational methods coming out of bioinformatics and other fields to kind of untangle the mystery
of how these languages are related. So the approximate number of Dogon language speakers
are listed here for the languages that we kind of know something about. Of course, population
figures are yes, they're a hit or miss type of thing. And we could argue that the languages in
the right hand side are rather endangered. So a lot of these languages are spoken in very difficult
places. There's climate change going on in this particular area, desert desertification from the
Sahara, it's coming into the regions. And so many of these people are moving to the plains where
food is more abundant. And then they take, they start to adopt the larger Dogon languages. Or at
the same time, those people are moving into the cities and adopting major languages like French
for upper mobility purposes. Then there's also another factor, which is Islamification. So there's
more Islamic culture in the north. And for whatever reasons, a lot of the traditional Dogon things
like musical instruments are no longer accepted into the culture. And so what we see is from the
north to the south, there are actually less vocabulary for more traditional Dogon items. If
you're familiar with the recent news, the northern part of Mali has been taken over by extremist
rebels. And they're destroying a lot of the cultural artifacts. And this is getting in towards the
northern part of Dogon country. So even though things like war are kind of rare for language
endangerment, this could play a factor. So as I said, the internal structure of the Dogon family
is relatively unknown and Dogon languages relative to other languages are unclear. Dogon has some
unusual typological characteristics, it doesn't have noun classes like a lot of the languages in West
Africa. And it has a different word order, which is more like Mande. This is to give you an idea of
the classification of Dogon. So you can see that it's been classified as nearly everything. And I
actually needed two slides to put everything on there. In recent history, it's been considered
just kind of unclassified, or it's been pushed all the way to the top of Niger Congo with its own
branch. So I'll speak briefly about the data collection process. So this is the typical field
linguist situation, you have a linguist like Jeffrey Heath in the field. And he's working with a
speaker of language A says, how do you say, x, speaker response x, he probably asked the poor
speaker a dozen times to repeat himself. And this situation is recorded. And then it starts with
phonetic analysis. And over time, then it becomes the phonemic analysis, which means that the
linguists has identified the contrast of sounds in the language. And then this data is put into a
word list dictionary, as you get passwords, you start to build grammars. And these different
languages are then collected in different word lists. And then we step in and we do parsing to
put that data into a format that we can use for our statistical methods. This is the happiest picture
I could find of Jeffrey Heath. And he's standing in front of this traditional wooden door, which if
you remember the previous slide where they're destroying the mosque, that's one of the it's
probably not this particular door, but that's one of the missing pieces. So I take these different
word lists and use RDF as a data framework to align the different words from the different word
lists on the same meaning. And this is combined together into this word meaning language format.
And you can see here that if you start with Bentay and go down through a couple languages, you have
something that might be related. It kind of looks the same if you can read the international phonetic
alphabet with its particular Africanist twist here. And so maybe the last two were also or the last
the two before the last word might also be related. Parsing these have issues, right? So we use
standard Unicode. If you have this is an example that I made up if you have an original string that
I won't try to pronounce. If you parse that on Unicode code points, of course, you get TS raised
H O and the diacritics. If you say Unicode, put those things together for me, then you have these
characters. And then what we actually need is the graph themes or the phonemes in the language to be
able to do analysis. For that we need language specific, what we call orthography profiles,
where we speculate or we specify which particular groupings occur and those should be treated as
individual items. And then out of that comes the parse dictionary format that we can put into the
statistical methods. So you see here we have the graph themes into individual units. And we can
also convert those then into phonetic alphabet. I will say something about the statistical methods
that we are trying to use to resolve the problem of how Dogon languages are related. The data we are
working with consists of almost 10,000 concepts or words that are collected at 17 Dogon villages.
So it's a huge data set, it's a huge word list. It's IPA transcribed, it's parsed, and that's all
nice. But the problem is that we have no information which words are cognates and which are not. And
most of the state of the art methods nowadays used in computational historical linguistics are using
cognates in order to resolve the genealogical relatedness of languages. Unfortunately we don't
have that, so we decided to try the automatic cognate identification. That's a very challenging
task for computational linguistics. Since cognates can have, so cognates are words that have the same
origin, they come from the same proto-language. They can have similar meaning and similar forms,
and that's a very fortunate situation if that's the case. However they can have very different
forms, and they can also have very different meanings, sometimes they can have opposite
meanings. So it's very difficult to identify them. We tried the two approaches that are nowadays
considered state-of-the-art approaches, and they're very different in their nature. The first is
feature-based approach, or phonetic comparison approach, where two strengths are compared by
representing every phone as a set of features. And the second approach is not dividing every
phone into a set of features, but it's rather grouping all phones into ten major classes. So
feature-based approach that we use is articulatory features, and that is very often the case with
phonetic comparison algorithms. One of the reasons is that articulatory features are relatively easy
to quantify. So for vowels we use four features, for consonants we use nine features, and we
additionally weight them. There are several feature comparison models around, and they're pretty much
similar, they differ in some detail. Algorithm that we use to align strengths is Niedleman-Wunsch.
It's basically weighted added distance algorithm, or weighted Levenshten algorithm, and both of
these algorithms are looking for the globally optimal solution. So the output of the algorithm
are aligned strings, so every word is pairwise compared to every other word, and we also get
the distances between, or similarities between each two words. Based on the similarities of words,
we group them into clusters. We use a special version of the UPGMA algorithm that's doing flat
clustering in this case, and it only requires a certain threshold. So we don't have to define the
number of clusters in advance, because we don't know how many potential cognates are in a set. We
give it a threshold, so for example 0.8, and it stops grouping once it finds words that have
similarity 0.8 on the scale from 0 to 1. The outputs are, you can see here the outputs are
different, so different clusters have different colors here, and we see that it's successful in
finding words that have, that sound similar, that have, that they're similar on the surface, and
they're potential cognates. So they have similar form, and they have similar or the same meaning.
However, this method is not able to save these words are real cognates, or maybe borrowings
from one language to the other, so we cannot qualify them as cognates, and we use word homologies.
So this is the term we came up with. These are dog and villages that we work with, so all these
villages that are going from north, on the left hand side, going from northeast to the southwest,
are on the plateau, and these villages on the, more on the right, are on the plain. We define
the distances between these two villages as the percentage of the shared homologies, and I will
not go into the details, I will just show you the map that's actually showing that there are three
distinct groups in our data, so two villages in the south, and are really distinct, and then two
villages in the plain, while all the villages on the plateau are forming another group. And so we
see that they're really nicely geographically coherent. However, some work that we're doing now
on the native South American languages, and on dog and languages are pointing out that phonetic
comparison algorithms are not, maybe not the optimal solution in historical linguistics, and
some other work by other colleagues also came to the conclusion that it might not be the best
approach. So we tried another approach that's not assuming any universal phonetic space or
universe, yeah, that we can just compare sounds from different languages easily. This approach is
implemented in link pi. It's treating, it's putting all phones into 10 major classes. These classes are
classes proposed by Dolgo Polsky in 60s, and he came up with them by examining 400 sound changes in
400 languages, and noticed that some sounds are more likely to change, that all sounds are actually
more likely to change into sounds in their own group than into the sounds into the other group,
so these classes are completely empirically based. And in this approach also, probabilities of one
sound into the other sound depend on the position in the word, and that is what is here called a
prosodic string model. So some sound changes are more likely to happen at the beginning than in the
middle of the word, so that's what we know from historical linguistics. So this approach is more
linguistically, historically oriented. This is just to show you the sound classes that are used,
they intuitively make also a lot of sense. The procedure we use in the second approach is the
same in the first to find the similar, we find the similar words, we cluster them using UPGMA and
calculate the distances between villages using the percentage of shared homologies. This is the
output of the algorithm, so it's also coloring differently potential cognates, here cognates,
so it makes intuitively makes a lot of sense, they're pretty similar, and additionally this
algorithm multi-aligns all the strings, so within a cognate set, so this is a very good tool for
automatic multi-sequence alignment in linguistics. However, this method is unfortunately also not to
distinguish between real cognates and borrowing, so we have words that are similar and that have
similar meanings, but we can only call them homologies. We do also MDS analysis based on
the distances obtained by the second method, and we actually get a pretty similar picture, although
methods are completely different in their nature, we are getting very stable results when it comes
especially to these two villages, so they're forming a different cluster and they're probably
not very closely related to other Dogon languages, and these languages that are on the plane are also
quite different from the languages on the plot, which makes sense since communication between them
is very hard, they cannot climb very often to the hill. So these two methods are different in
nature, they're giving similar results, these are just the first results because we don't have real
cognates yet, we have only homologies, so what we are planning to do now is to go into the direction
of the loanwords detection, and then we hope to get an even better picture, better result picture of
the relatedness of Dogon languages. The first part of our project was pretty technical, so we
were helping languages that are in the field to document the language and to do error detection,
and they were very grateful, and it's a very nice cooperation. When it comes to quantitative
language comparison, state-of-the-art methods are able to find homologies, they are still not able
to find real cognates, and it's a real problem if we want to do any real historical linguistic work,
and what we are getting now by comparing homologies is a cyclonic comparison of language
varieties, which in this case is also a good thing because we don't know how they are related at the
moment, so if you resolve that problem then it will probably be easier one day to resolve their
real genealogical relatedness. We hope to get a better picture of how Dogon languages are related
and what is actually going on there, and we hope to bring recognition to the various distinct Dogon
languages because we hope that that's a good way to save some of them or to preserve them for a longer
time. Thank you.