We have tools for language documentation.
My two co-authors could not be here,
but the spirit will shine on every page, on every slide.
Tom Myers, in particular, chief technical officer
of the Entopus software, a small software company in upstate New
York, sends his best regards.
Entopus, in case you're wondering,
is a data structure that looks like an octopus,
but has n legs, where n is an arbitrary integer greater
or equal to 10.
Acknowledgments supported by several NSF grants participants
include an international crew from Russia, Japan, Alaska,
upstate New York.
In outline, fortunately, Sebastian
has covered much of the material of the first bullet here,
which gives me a fair chance to finish my talk on time.
The outcomes of the process of language documentation,
specifically archive, and the local community.
And in the end, I will address community needs
and how we intend to address them.
So language documentation, this is pretty much covered.
I would like to only emphasize that in addition
to a new linguistic discipline with new technologies,
there's also now a new attitude that when a linguist goes
to work in the field, the linguist
is not an agent working on inert nature of speakers.
But rather, there's a process of collaboration, which results,
in effect, in two sets of materials,
one for the community of scholars
and the other for the community of speakers.
And how to adjudicate different claims and tensions
from those different community, from those two communities,
was the subject of a talk held today by David Nathan.
And I will, as I said, address those later.
So in effect, there's a de facto standard
for language documentation, which builds on two concepts.
And looks like everybody is a linguist here,
so I won't have to explain much about interlinear glasses
and time alignment.
But very briefly, interlinear glasses look like this.
You have some units, a sentence, perhaps
with the commentary or translation,
broken into words or divided into words.
And then words are further divided into morphemes,
so we have the tree structure.
And we note that in transition from word to morpheme
or from sentence to word, we subdivide.
But in transition from morpheme to morpheme glosses,
we provide a symbolic label for each one of them.
This is important, and there's actually
concepts coming from Elan.
Now, time alignment is the process
of breaking a temporal entity, a clip, into pieces or segments
and align them with the corresponding stretches of text.
And text is a spatial thing, so you
can manipulate it much easier.
And so you need machinery such that you
can select a temporal segment by picking on a spatial segment.
And then you can navigate your information by segment
or go randomly to a segment you want.
So in effect, this makes temporal data into a random access
things, the way, say, book made scroll, the temporal access data.
And this makes it possible to create a table of contents,
to create an index, and most importantly,
to annotate individual segments, which is exceedingly important.
This is a page from Elan, which is a tool for basically bringing
together temporal data and interlinear glossed text.
What we do is we take the timeline presented here,
and we have a very convenient waveform.
And then we place the transcription onto the timeline.
And then underneath, we develop the same interlinear glossed
structure, but it is now connected
to the flow of sound, the flow of video, and the time.
Another example of interlinear glossed structure
comes from a different program, FLEX,
from Summer Institute of Linguistics.
I should maybe, for a moment, take a detour
and say that two major players in creating software
for language documentation are, of course,
MPI in Nijmegen and Summer Institute of Linguistics
in Texas.
And don't argue, that's true.
But unfortunately, they don't work together very often.
And that's one of the problems that we are trying to solve.
But proceeding with FLEX, you can
see that it has, at the bottom left,
it has two worlds, two big labels, texts
and words and the lexicon.
And what FLEX does well, and Alain doesn't do at all so far,
is to integrate the development of the corpus
and the development of the lexicon
In the ideal world, you go through your corpus of texts.
And when you encounter a morpheme that's already
been described, then the interlinear gloss
should be filled automatically.
And if a new morpheme is encountered
and an interlinear gloss provided,
then this morpheme must be immediately
and automatically added to the lexicon.
So that in the end, you have both.
And then in the final product, you
have links between the lexicon entry and the tokens
of that entry in the corpus with further links
to the video segments.
So that starting from the lexicon,
you can say, oh, I would like to see
the genitive plural of this noun.
And you search for general plural.
And you observe the speakers of the language
articulate the genitive plural and their facial expressions,
what they feel about general, genitive plural, and so on.
And conversely, each word in the corpus
has a link to its lexical entry in the lexicon.
So flex does several things very well,
except unfortunately, until February this year,
it knew nothing about media files or time or time
alignment.
In February, they updated the XML schema
for the corpus text, including new attributes, media files,
time, offsets, globally unique identifier, good,
which is essential for any collaboration between two
programs, especially programs whose view of the world
is very different, and the speaker.
And if nothing else, the achievement
of our project is that we nagged them long enough
to introduce those attributes.
So yeah, there's dozens of desperate messages going back
and forth.
Anyway, Ilan is, of course, an extremely well-designed program.
It's just a pleasure to work with Ilan.
And the developer, Han Stoyles, if I pronounce his name correctly,
he's a wonderful man and an excellent programmer.
There's an aesthetic pleasure in working with Ilan.
And so I think you can record multiple speakers
and gestures and sign languages.
Unfortunately, it knows nothing about lexicon.
And so there's a gaping hole there schematically.
Now, there is something called Lexan that was announced in January
of this year and is supposed to do for Ilan
what flex does for everybody.
But it's a closely kept secret what it is going to be and when.
Maybe we have some people from MPI here.
We can kind of shame them into disclosing more information.
But as of now, there's a hole in Ilan and a hole in flex.
And that's what our project really is about.
We want to be able, and actually it started with the users,
with linguists, who wanted to have this simple workflow.
Start in Ilan, transcribe time align at the sentence level.
And then export to flex.
And then in flex, you do the lexical and morphological analysis,
which is flex does very well.
And then you export the result back into Ilan
with all the additional tiers that were provided in flex
was morphological with lexical and morphological analysis.
And eventually, the next step will
be to allow for repeating these in a loop
because you export back in Ilan.
You listen to it.
You may discover that some edits are necessary or has happened.
In our case, somebody from Kamchatka
told us that this particular annotation is not right,
so it has to go in circles.
But basically, the simplest case that we are working on now
is shown diagrammatically here.
And the switch from red to black indicates
where we are in the development process.
We're almost done.
And all of this is now implemented
with a group of people working on Itilmen.
Itilmen, also known as Kamchatal,
is a practically extinct, isolate language of Kamchatka.
And some people said that at this point,
I should show a map of Kamchatka.
But I assume that everybody in this audience
knows where Kamchatka is.
If you've ever played the game strategy,
then Kamchatka looms very large there.
So what are those Itilmen projects?
It's actually a very nice combination of two projects.
One of them is there's this accumulated body
of video recordings collected by David Curster of an anthropologist
at University of Alaska and Jonathan Bobolick.
And they want to do that workflow that we described.
They want to transcribe it in a land and then go to flex.
And in the end, have Coricorpus and lexicon.
In addition, there are those 12 boxes of paper cards
in Leningrad of Petersburg in the Institute
of Linguistic Studies that Alexander Pavlovich
Wallodin collected 50 years ago.
And so when he dies, and he's closer to that moment
than most of us here, probably, then it
will be very difficult to interpret those cards.
So the grant is to scan, enter, transcribe, and create
a dictionary.
And this is what Chikaka Ono of Kubi University in Japan is doing.
And then, of course, we want to merge the lexicon that emerges
from the corpus and the lexicon that emerges
from those legacy materials.
And it will be interesting to see
whether language has changed over time and so on.
And also, as part of that project,
there is work on flex alone interchange that I described.
And in addition, a project to build a community website.
Website is really misnomer because the community in question
doesn't have laptops and doesn't have internet access.
They do have cell phones.
And so we'll have to eventually port it all to Android phones.
But anyway, this slide is from David Kerster.
And it describes the entirety of all these projects.
So it has the lexicon and the corpus with the line
and flex collaborating.
It mentions all the sources for lexical materials,
beginning with Krasheninnikov at the top,
an 18th century Russian ethnographer, the first Russian
to be a member of a Russian academy, the last with Germans
at this point.
And Yochil Son, who was from a Jewish family in Vilnius,
but then unavoidably became socialist, went to exile,
started doing ethnography, migrated to America,
worked on Jessup expedition under Franz Boas.
So all of this we are bringing together.
And at the end, in the bottom over there,
you can barely see it, but it says community interface.
So Flex-A-Lan project history has basically been described.
I will just mention that our approach for bringing those
to program together is to develop an interlingua,
in effect, for them, and an intermediate representation
that can store all the information from both programs
and then selectively deliver to each program
the information that it can understand and leave aside,
but keep for future use information from that program.
Data formats, tables, trees, and graphs.
And the main point here is graph is the most common format.
We can describe tables as graphs or trees as graphs.
And so our underlying representation
is a graph with nodes and links.
And it's a semantic graph because we
think of those nodes as subject and object
and links are predicates.
So for instance, we can say that some globally unique identifier
object is of type human, and it has label Socrates,
and human is a subclass of mortal things.
And we can deduce from that that Socrates is mortal.
Table can be described as a graph by just saying
that taking the row label and the column label
and the content of the cell and making them
into a subject predicate object.
A tree can be described as triples by saying
that this object is a root, and it has a tag name document
at depth 0, depth goes down.
And then there's a first child, and then there's a sibling,
and so on.
You proceed in this way to meticulously transcribe
a tree as a graph.
And of course, we would go crazy doing this,
but computers are good at that.
And then this huge graph, RDF graph,
RDF stands for resource description framework,
is the data from which everything is built,
including the community website.
We believe that the community of scholars
and the community of speakers are different communities
with vastly different interests, vastly different goals,
technological capabilities, and so on.
And so they deserve two different interfaces.
The community goals are to preserve memories
and teach the language mostly.
And of course, the annotated digital video
is perfect for teaching the language.
I skipped the slide, but I will return to it
if I still have time.
One minute, OK.
So for instance, last June, there
was a meeting of all ital men in Kamchatka, all six of them,
but plus many interested young people who are vaguely talking
about revitalization and so on.
And they were very specific about what they want.
They wanted, first of all, personal histories, obviously.
They wanted a dictionary.
They wanted flashcards.
They wanted people to learn their words.
They wanted minimal pairs to show how this sound is
different from that sound in identical content.
And so the plan is to generate annotated video in our format.
Remember that time alignment was invented at Colgate,
and we do have a player for time alignment,
time aligned, and multimedia materials that
is specifically for language learning.
And we will port that player, which
is a Java program, to Android phones
and ask for money to buy at least six or maybe 50 Android
phones and send them to Kamchatka.
Conclusions, time alignment is great.
Digital video, time alignment, and interlinear glossing
are good for language documentation,
and they're also great for language learning.
Building the corpus and the lexicon
should be integrated in ways high-indicated,
and RDF graphs are very good for data integration.
Thank you very much.
Thank you.