I found very useful for thinking about patterns
of historical narration.
So he developed a metaphor between places and events
and suggested that we can think of histories
as picking out itineraries through the event-worthy field,
the field of all possible events.
Now, I think the itinerary is a useful metaphor here
because it highlights how narrated historical events
have a dual nature.
On the one level, there's shared concepts
that link different histories, but on another level,
each history develops its own concept.
So if we look, again, using the analogy of a place,
we can think of San Francisco as a single place
that unites various possible itineraries
we can make through the Western United States.
And your experience of San Francisco
is going to be a bit different depending
on the places you went to before and the places
you visit after San Francisco.
On the other hand, even though there
is some fact of the matter about an administrative place
called San Francisco, if we actually
look at people's movement through space,
San Francisco is really just a name
we give to many possible experiences.
What we're looking at here is photographs
taken in San Francisco and posted to Flickr.
The points in blue are photographs
that have been taken by natives.
The points in red are photographs
that have been taken by tourists.
And we can see by the different patterns
here that, to some extent, locals and tourists
visit or live in different San Franciscos.
And this is just one possible breakdown
we could make of itineraries through San Francisco.
And we could do this in any number of ways.
It doesn't need to be locals versus tourists.
It could be young versus old.
We could look at all sorts of various demographic groups.
So my argument is that, likewise, each history
provides a unique view of the past
by picking out its own path through the field
of possible events.
But because histories responds early histories,
including the first drafts produced by eyewitnesses,
these paths intersect.
And the intersection of the more well-traveled paths
lie events which become taken for granted as landmarks,
sites to be visited on a pre-arranged itinerary,
marking out the recommended scenic route in the beaten track
from one major point of interest to the next.
And that's a quote from Anne Rigney,
who studied histories of the French Revolution,
trying to understand how these certain events became
these kind of obligatory points of passage
for people writing about that event.
Now, when we get these very kind of solid patterns,
they can become hypothesized
as particular named events or periods.
And periodization helps us perceive meaningful order
in the flux of the past.
But if we accept these itineraries too readily
without reflecting upon the scope of the views
that they afford, we risk missing opportunities
for new insights.
Historical insight results not just from the accumulation
of new facts about the past,
but from the development of new views
upon the facts that we already know.
And the more stories we have about some piece of the past,
in the words of Frank Ankersmit,
the deeper our insight into it will be,
not primarily because each story will mention facts,
not mentioned in the others,
but because only the presence of other stories
enables us to draw the contours
and to recognize the specificity of the view of the past
presented in each one.
So I'm currently in the early stages of a project
to test Ankersmit's theory by constructing a system
for comparing and contrasting the histories
of the Civil Rights Movement in the United States.
The historiography of the Civil Rights Movement
provides a compelling example of how periods
coalesce around dominant stories.
So the first generation of civil rights scholars
conceived of the civil rights struggle
primarily as a political movement
that secured legislative and judicial triumphs.
And the typical chronology of this movement
begins with the US Supreme Court ruling
in Brown versus Board of Education of Topeka in 1954,
and ends with the Voting Rights Act of 1965.
And due in part to the intense media coverage
of the events of this era,
by the end of the 20th century,
this chronology had become a well-beaten path,
not only among scholars, but also in popular understanding.
This is generally the chronology you'll see
on the Wikipedia pages about the US Civil Rights Movement.
This is what you'll typically see in textbooks
in the United States.
In the past decade, however,
a new generation of scholars has sought to broaden
this itinerary, telling the story
of a long civil rights movement
that took root in the liberal and radical milieu
of the late 1930s, was intimately tied
to the rise and fall of the New Deal Order,
accelerated during World War II,
stretched far beyond the South,
was continuously and ferociously contested,
and then in the 1960s and 1970s,
inspired a movement of movements
that defied any narrative of collapse.
So the concept of the long civil rights movement
is about more than simply replacing one story
with a new one.
By widening the scope of the civil rights movement,
it opens a space for a greater diversity of stories,
and this diversity enables insight,
but it also makes it more difficult
to grasp the movement as a whole.
So I mentioned whether computational analysis
might complement the work of scholars
evolving new periodizations and perspectives
by providing tools for perceiving patterns
in this more complex whole.
So in my project, we're applying cutting-edge
text processing techniques to two corpuses.
The first is 70 books and a selection
of papers and articles made available by the UNC Press.
The second is 350 interviews conducted
by the Southern Oral History Program
as part of their long civil rights movement initiative.
The specific techniques that we're using
are event parsing and narrative clustering.
So event parsing involves identifying sentences
that communicate some event, for example,
a strike, a protest, a bombing, or a legislative act,
and identifying within or across sentences frames,
which are these conceptual structures
that describe kinds of events,
along with their participants and settings.
So we're using frames from a project
that's been going on for about 15 years
at UC Berkeley called FrameNet.
This is an example of a frame.
You'll see that here at the bottom we have,
these are all the different words
that can invoke this frame that's been named attack.
So in a way, what we have here is a kind of thesaurus,
but in addition to the typical
kind of paradigmatic access of a thesaurus,
where we have all the kind of different words
that mean sort of the same thing,
FrameNet also gives you this syntagmatic access,
and it involves different roles
that are associated with those frames.
And some of those are specific to the frame itself,
others are like place and time and purpose or reason
can be applied to any frame.
And we can use frames to annotate sentences.
Here, this is a sentence that invokes
the attack frame, and we can select particular words
or particular phrases in the sentence
that fill the roles related to that frame.
And because FrameNet has been annotating sentences,
they've provided data that people have used
to train statistical parsers
that can try to do this annotation automatically,
and those are the kinds of tools that we're trying to use.
So just to give you a sense of the work we're trying to do,
we did a small preliminary study
where we focused on one specific event,
the 1963 bombing of an African American church
in Birmingham, Alabama.
The purpose of our study was to find out
if we could automatically relate text to frames
and then use those frames to automatically construct
a representation of this event.
So first, we manually searched the corpus
for passages referring to or discussing this event,
and often this was just a single sentence,
sometimes longer passages of up to a few dozen sentences.
And we eventually identified 33 passages
that referenced or discussed the 1963 church bombing
drawn from five full-length text
and 16 oral history transcripts.
We then annotated each sentence with FrameNet frames,
and we used the semaphore frame semantic parser
to automatically detect these frames
and their participating entities.
So here's an example of two sentences,
both referring to the 1963 bombing
of the 16th Street Baptist Church.
And you'll see that actually they don't share any words
in common except for 1963
and then some articles and things like that.
So what if we look at the frames
that are invoked by these two sentences
and can this help us establish a link?
So here are the frames that semaphore detected
in these two sentences.
And here are the entities, the particular words
and phrases from those sentences that were picked out
as filling particular roles in those frames.
And we see that even with the output
from the frame semantic parser,
we still don't have any frames in common.
So what we're trying to do now is
can we learn the relationships among these frames
that would allow us to make these connections?
And then learn this kind of cluster.
This is what we're trying to do right now
is learn a cluster of frames and associated entities
that we could say this is somehow a representation
of the 16th Street Church bombing
that we could then use as a scaffolding
for exploring this corpus.
So what are the problems we have?
Well, in this preliminary study,
we were working with a limited set of passages
that we already knew referenced the same events.
What we're trying to do now is scale up
to the entire corpus that will give us more data
to learn the relationships that we need
to learn among frames and entities
in order to make those connections
that are currently missing.
And we're also drawing upon some related
natural language processing projects
that have tried to learn these frame relationships
on much, much larger corpuses like news corpuses
where we can kind of use their relationships
that they've already learned, use them as is.
We've seen a December 4 parser.
Well, it's fairly good at identifying the frames.
It's weak at assigning the roles in those frames.
So it often identified the church as the assailant
in the sentence to do with bombing,
which is obviously getting things quite wrong.
And finally, we need to do additional
named entity recognition and co-reference resolution
in order to link the entities.
So often the entity that's recognized
as being related to a particular frame
is a pronoun like he or they,
and we need to link that to the actual proper name
that that refers to in order to create these links.
So this is ongoing work.
We're currently doing a manual annotation
to enable quantitative evaluation of the frame parsers.
Right now we have a qualitative sense of how they're doing,
but we'd like to be able to quantify that.
We're trying a rival project that's slightly different
called PropBank, which has done their frames
in a slightly different way.
And we're experiencing with different clustering methods
to relate these frames to one another.
So as I said, we're still in the very early stages
of this work, but I'm happy to announce
that just this month we received a grant
from the Institute of Museum and Library Services
that will fund this project for three years.
And so we hope to produce some reusable tools
that other people can use to try these frame semantic
parsing techniques in other projects,
and also to try to develop some new kinds of tools
for interacting with the oral histories
that use these frame structures in interesting ways.
And thank you very much.
Thank you.