Category: work

Whew, that was grand. Just one thing about the closing panel discussion, while it’s fresh in my mind.

This year’s CaSTA was billed as “a joint computer science and humanities computing conference.” And it was! And [we saw that] it was good. Of the five keynote speakers, three were humanists and two – computer scientists. The final discussion was called “Humanities Computing Science??”.

William Arms, in his remarks during the panel, said that during the conference a word was frequently used that isn’t generally used in his usual [computer-science] circles. That word – knowledge. He, and just about everyone at the panel, said that what they primarily want from the “other side” is dialogue.

In light of that, what I’d like to see in this continuing dialogue is a bit of discussion of the word science. As it’s been used lately (in the last, what, 200 years?), it implies “HARD.” Humanities implies “soft.” That’s a major point of contention.

But given that “science” pretty much means “knowledge,” should we revisit our use of the word?

Ray Siemens is a computing humanist and Renaissance scholar working at the University of Victoria. The full title of his paper is “Knowledge management and textual cultures? Work toward the Renaissance English Knowledgebase (REKn, pron. “reckon”) and its professional reading environment (PReE).”

REKn seems to be aiming to amalgamate and integrate knowledge in its area. Its implementation is based in the study of disciplinary activity of, and professional interaction among, those in the humanities. It’s founded in concepts of knowledge representation and modeling. A short description of the project can be found here.

Knowledge representation: draws on the field of AI and seeks to produce models of human understanding that are tractable to computation. Modeling: REKn/PReE model data, intellectual processes, and beyond.

Key elements of REKn’s model:

– representation of archival materials

– analysis/critical inquiry originating in those materials

– the communication of the results of these tasks (the dissemination of primary and secondary materials)

REKn’s assumptions: all of the above are interrelated and inseparable, and electronically representable.

They’ve collected primary and secondary sources, and have built tools for working with them (the tool-building process seems to have been multi-stage: many tools built and discarded as inadequate). They’re looking to long-term partnerships with Renaissance materials providers in the future. Right now REKn has about 13,000 primary sources and over 80,000 secondary sources. About 1500 of these resources are currently available for public use, but the majority are not open-access.

So that’s REKn, the text base. What about PReE, the reading environment? It’s a rudimentary document viewer, and analysis and communication facilitator. Currently the UI is a “down-and-dirty prototype,” as they’ve been concentrating on making things work in the back end. [vz: he’s showing PReE in Windows; I wonder what it’s written in.] They’ve made several analytical tools for the encoded texts. Primarily, though, analysis will be carried out using TAPoR tools.

Communication facilitated electronically: they’re attempting to provide a system by which people can manage their professional interaction.

Short-term goals:

– integrate better with TAPoR and the Public Knowledge Project reading tools

– conduct usability studies

– consult with “contextual” stakeholders, including acad. publishers

– move prototype to a web environment

– scale up!

And that’s Ray’s talk, the last talk of this conference. Next is the panel discussion, titled “Humanities computing science?”. The panel will consist of the five keynote speakers. I’m not sure whether I’ll be taking notes on this; it’ll be video recorded, and there’s little probability that I’d do it justice. Again, I’ll update when the webcasts are up.

Eugene Lyman is an Early English scholar at Boston University. The full title of his paper is “Presenting an electronic critical edition of Piers Plowman B.” The B refers to one set in a multitude of manuscripts of this Middle English alliteraative poem, comprising only one of its versions.

EL frames his talk with the following two quotes:

“Computers compute, of course, but computers today, vfrom most users’ points of view, are not so much engines of computation as venues for representation.” –Matthew Kirschenbaum

Understanding the poetics and principles of electronic scholarly editing means understanding that the primary goal of this activity is not to dictate what can be seen but rather to open up ways of seeing.” –Martha Nell Smith

Lyman has created software that allows you to look at the existing manuscript pages from ten different manuscripts, enlarge portions of those pages, read the transcriptions, look at erasures that tell us interesting things about how people might’ve edited in medieval England. You can search within the dataset, visualize the text in various ways, view the underlying XML markup… yesterday (or was it the day before?) EL actually gave some of us an informal demo of this thing, and it is sweet.

How to make a continuity of presenting a single text that exists in multiple manuscripts?

EL created the Elwood Viewer, which looks at documentary editions of texts. Its aims:

– tight coordination of text and image

– visual cueing to guide/reinforce reader’s attention

– handy tools, all within a metaphorical arm’s reach

– ease of navigation, especially at opportune moments

– parsimonious use of screen real estate

– simple, no-cost programming environment, open to change [this is all JavaScript, I think… -vz]

The software allows you to extract data for further analysis. One such analysis called into question the notion that scribes were totally random with respect to the ways in which they encoded [marked up, oh yes, for markup exists in many forms including punctuation and embellished first letters] their texts.

This is all a cricital edition: you take a group of witnesses, compare them, note the variations, and choose the variations you think were on the author’s agenda when she was writing the text. The notion of a critical edition, which effectively “leaves behind” the actual manuscripts, is pretty controversial; nevertheless, at the moment critical editions have a lot of weight in literary studies. [vz: I’m leaning toward the camp that’s sceptical of critical editions, but only in the sense that I don’t tend to consider them definitive, while many others do.]

EL has a prototype version of the critical edition. I haven’t found any screenshots of it on the web, but here’s the Piers Plowman Electronic Archive hosted at the University of Virginia. It seems more a worksite than a ready-made resource, but is worth poking around.

Johanna Drucker is a professor of media studies, and a founding member of UVA’s Speculative Computing Lab. The full title of her keynote is “Graphic conventions: visualizing knowledge and subjectivity.”

Can we shift from information to interpretation, and shift [back] to a more humanistic point of view within digital humanities? To do that, we have to re-introduce subjectivity, and maybe substitute the mechanistic with the probabilistic (the latter being humanists’ worldview, according to JD).

Visual information conveys information in a form that makes it very hard to analyze systematically. Two ways to create stable knowledge in a notation system: one is with [natural] language, the other – mathematical notation, said Rene Toms of the Oulipo. He never did talk about visual representation, and with good reason: it’s an unstable notation mode.

Subjectivity comes in two forms: position (structural) or inflection (semantic).

The notion of information comes from a particular set of assumptions of what knowledge is. JD not interested of getting rid of this model of knowledge; but rather to propose another model of knowledge.

Visualization is compact, the problem isn’t having enough space to represent information – it’s pinning down the exact nature of the information and our assumptions about its aspects.

Visual information (VI) can lie or misinform, like natural language can.

The silliness of chunking of processes (how authors write stories: “author thinks about a topic” –> “author sketches an outline” –> “author reviews the sketch”) is apparent, but we have to do this sort of chunking when we’re working in a computational environment, which requires discrete units. Because of schematics’ rhetorical power, we eventually come to believe them.

So what about text visualization (as opposed to data visualization above)? Interest in doing things to and with texts is very active, especially within the creative-writing communities. Like TextArc, that sort of thing. They can be silly, ugly, destructive of the original, and yet they have their uses.

Edward Tufte, the exquisite engineer according to JD: information pre-exists visualization. Visualizations can be transparent enough to get us access to information. JD disagrees: visualizations are interpretive, opaque, distortive. They create informmation.

Temporal modeling at SpecLab. Basic assumption: timelines as they are conventionally defined and designed come out of the empirical/natural sciences. Assumptions there: time is unilinear; time is homogeneous (metric is stable); time is continuous (no unbroken intervals in temporality). None of these three things hold. Temporality branches in our lives, in poetry/film/etc. Time is not homogeneous – some moments fly by, others are long (the moment before the kiss and the moment after are very different, JD says). Time is not continous, either: there are breaks/ruptures, recorded in historical accounts for example.

SpecLab constructed a grammar of inflections, of visual elements they’d use to represent time, types of events and their relations to each other. There’s a lot of information JD is giving about what SpecLab has been doing; I’ll point you to the Lab’s site instead of summarizing.

In the IVANHOE game, every action takes place from within a role. Each role has a set of assumptions that go with it. They employed it as a teaching tool at UVA, with the purpose of showing that, in fact, every action stems from a set of presuppositions. [vz: that can’t be right, I’ve captured too simplistic a description. Go see the site for more.]

Subjective meteorology: JD’s current project. An art project, which JD says – duh, art is in the humanities. [vz: yay!] She charted and graphed and visually represented a bunch of weather patterns – lines of anxiety/anticipation, storms of anger – which look gorgeous on the slides but don’t seem to be on the web. These representations can be chained together and animated to playfully and visually represent one’s subjective perceptions of the world around.

Great discussion follows. I can’t pretend to capture it well enough; I’ll post an update when the keynote webcasts are up, and urge anyone interested to watch this one when it’s available.

David Hoover is at NYU, and is the Vice-President of the Association for Computers and the Humanities. The full title of his paper is “CaSTAing breadth upon the waters.” (“Cast thy bread upon the waters: for thou shalt find it after many days,” say Ecclesiastes 11 – hence his title.)

DH seeks simple methods for examining word frequencies in corpora of single authors, at different stages of their production. Do authors tend to start disliking (using less) words they used to like (use a lot) earlier in their careers? Or vice versa? How does an author’s vocabulary change over her production’s life? DH does a lot of statistics to try to find out.

He’s talking about Trollope, about whom I know nothing. Apparently, the 100 most variable words in his corpus are all proper names. They also all appear in more than one stage of his writing career (early-middle-late). Henry James, however, has some non-proper nouns.

DH’s project is very much in progress; he says it’ll be a while until he has something interesting to say about the evolution of writerly language. One interesting question is: if you have a writer who starts writing very young, does their vocabulary change quickly, early on? What about writers who write far into old age?

One interesting consistency in James is that he seems to have used fewer “precious” nouns as the years went on: words like coquette and tresses.

Richard Cunningham is at the Acadia University English department, and directs the hypermedia center there. The full title of his paper is “Developing digital navigation from The Arte of Navigation.”

Readers experience something different reading an electronic document as opposed to a paper one. [Glaringly obvious, RC admits.]

RC presents the Acadia Digital Culture Observatory. They have digitized a 1561 edition of The Arte of Navigation and want to observe how readers read and use it. The original text included a navigation instrument made of three concentric paper circles of different sizes (volvelles), which are to be overlaid one on top of another and rotated. Here, you can see it for yourself. (Hm, it doesn’t seem to work in Firefox on Mac in Blackletter mode; I suggest the use of Arial to be safe, or you can download Blackletter from their table of contents.) Check out particularly the navigation instrument Flash files in the “other moving images” section of the TOC, they’re fun to play with.

Ray Siemens of Victoria hosts a session of three papers related to the Text Analysis Portal for Research. First we have Geoffrey Rockwell, with “Text empires: text analysis in excess.” Shawn Day will talk about “The use of the recipe as a guilding metaphor for flexible and efficient self-guided computing instruction.” Finally, Stéfan Sinclair will talk “On data & views in text analysis.” All three presenters are from McMaster University in Hamilton, near Toronto.

ROCKWELL.

Information overload: 5 exabytes of information created in 2002. Exabyte = 1,000,000,000,000,000,000 Bytes. It’s a thousand petabytes, or a million terabytes. [Holy wow.] Spam is cheap, but reading has costs. How can text analysis help?

Why this explosion of information?

– growth in population and wealth: more money, more media toys

– multiple-media, from the photograph (1820s) to the iPod

– digitization of information and business practices: cheap creation, storage, reproduction, and transmission

Challenges to the system: what are the effects?

– experience of information overload

– multimedia shock

– narrowing expertise (because nobody can’t keep up with a broad discipline!)

– archive fever

What can we do?

– understand the problem (literary dimension to it; a problem of scale, a bibliographic problem)

– produce less? [shock! I can hear the internal gasps around the room!]

– file and (not) store smarter

– find smarter (not more) [ooh, I’ll quote him in my dissertation work! no, I cannot, in fact, process all the litcrit written to this day]

– learn to read differently

The latter two of the above are opportunities for text analysis.

Problem of scale to text analysis for finding and reading:

– heterogeneous formats and multimedia rich

– closed (“for perfectly reasonable reasons” -GR) information empires (Google) build on existing indexes or build their own

– new questions, research methods (data mining and visualization)

– text analysis tools developed for coherent texts (collaborate with data mining & HPC [high-performance computing] community)

TAPoR.2 model, Beyond Finding and Reading:

– gathering and aggregation function (working with existing empires like Google; create your own study library (myEmpire))

– mining function (clustering and classification; provoking questions, not finding)

– interface and visualization function (effective interactions for research)

DAY

They’re using the recipe metaphor to get people of different backgrounds to use TAPoR.

A recipe for self-guided instruction:

– ingredients

– steps

– glossary

– discussion

– further information

Ingredients:

– ingenuity

– a useful metaphor

– a versatile set of tools

– users desirous or willing to consider using said tools

Steps

– identify objective

– consider users’ needs

– develop case studies that describe how your tools can meet these needs

– apply a familiar metaphorical approach to engage and instruct

– deploy recipes through a wiki

Glossary

– recipe: a useful guiding metaphor that offers optimal flexibility…. [couldn’t get it, too fast]

Further Information:

Try the recipes out! (For example.)

Nice, familiar, easy concept. As Shawn is pointing out right now, super easy to engage a beginner user. This could be very useful, as well, when getting folks used to traditional humanities research methods to try, say, text encoding.

SINCLAIR

[Stéfan is the creator of HyperPo, the coolest text analysis tool ever so far.]

Generally, there’s a one-to-one mapping between tools and the data views of their results. SS has been thinking more in terms of this progression:

text -> tool -> data (TAML) -> style -> view

Among other things, he wanted to create a framework to use in teaching the development of text analysis tools in a modular way.

It’d also be nice to be able to chain tools together – you run a tool on a text, get the resultant data and feed it to another tool, and so on. This requires tools that can ‘talk” to each other, and output data in the same (or similar enough, or easily translateable) formats.

HyperPo 7.0 is coming soon!

William Y. Arms is a computer scientist currently working at Cornell. The full title of his keynote is “Humanities and social science research using vast amounts of web data.”

Examples of very large collections:

– Library of Congress: National Digital Information Infrastructure and Preservation Program

– The Internet Archive‘s historical collection of the web (600 TB, terabytes)

– Large scale digitization projects: Open Content Alliance, Project Gutenberg, Google, Microsoft, Yahoo, etc.

– USC Shoah Foundation: Survivors of the Shoah (400 TB)

How will humanities and social science scholars do research on collections which are large by supercomputing standards?

“Only the computer reads every word” –Greg Crane

– Researchers interact with the collections through computer programs that act as their agents.

– Users rarely view individual items except after preliminary screening by programs.

– Collection requires a highly technical computer system that is used by researchers who are not computing specialists.

– The collection is a high-performance computing system.

– Use of the collection depends on automated tools, which require state-of-the-art indexes for text and semi-structured data, natural language processing, and machine learning.)

Example: the Cornell Web Lab (or is it a Library, asks Arms?)

The structure of text:

Manual analysis and mark-up

– skilled bibliographers and cataloguers

– manual textual markup

– semantic web tools for representing relationships (e.g., RDF, Fedora)

Semi-automated methods

– automated name recognition under human control (e.g., Perseus)

– expert-guided web crawling (e.g., iVia)

The above are tens of millions of records. How do we manage billions of records?

Example: The Internet Archive web collection

The data: complete crawls of the web, every two months since 1996, with some gaps:

– range of formats and depth of crawl have increased with time

– no data from sites that are protected by robots.txt or where owners have requested not to be archived

– some missing or lost data

– metadata contains format, links, anchor text

– organized to facilitate historical access to a known URL (Wayback Machine)

The research dialog between a scholar (S) and a computer scientist (CS) goes something like this:

S: Here’s a study we’d like to do…

CS: We don’t know how to do that analysis, but would this be any use to you,

S: Not as you suggest it, but here’s another idea…

CS: That might be possible, with the following modification…

BOTH: Let’s try it and see!

Eventually we get something that is both useful from a research point of view and feasible from a computing POV.

Social Science Research:

– the web as evidence of current social events (spread of urban legends; development of legal concepts across time)

– the web as social phenomenon (political campaigns, online retailing, polarization of opinions)

Research topic example: social and information networks, joining a community. Question: what is the probability an individual will adopt a new behavior, as a function of the number of his/her friends who are adopters? New behavior could be: adopting a new technology, joining a club, etc.

So, when everything is in digital form, will the library go from being the largest building on campus to being the largest computing system on campus? WA says there’s a good likelihood of that.

WA goes on to describe some of the projects on the Web Lab’s plate right now. Their descriptions can be found on the Web Lab site.

Policies issues on the use of the lab: custodianship of data; copyright; privacy.

Design guidelines for builders of large digital collections:

– every online collection or service needs an application program interface (API) for computers, not humans, to interact with the library.

– a primary methodology is: select a subset of the collection; download to researcher’s computer; use programs on the researcher’s computer to analyze the data.

– almost all metadata will be computer generated, but human cooperative editing can correct errors.

Brian Pytlik Zillig is an all-around digital-library tech wizard at the University of Nebraska-Lincoln (UNL), which hosted the first annual Digital Humanities Workshop a few weeks ago. The full title of his paper is “TokenX: a text visualization, analysis, and play tool designed for the XML document tree.”

Some history:

– CDRH and other digital centers significantly rely upon XML

– XML is a 1998 [whoa, old] recommendation [hunh, not a standard] of the W3C

– XML is a robust and flexible medium for content

– UNL has been using XML/SGML since 1998

– all CDRH projects use XML

Research question, born in 2004:

– can emerging standards assist in text visualization, analysis and play? (for example: XSLT)

BPZ’s goal:

– use XSLT to explore text visualization, analysis, and play (TVAP)

– provide TVAP options useful to facilitate the creative, qualitative, and quantitative exploration of XML text

Why another text analysis tool?

– there are good tools available written in a variety of languages, but none are created in XSLT, and none that takes advantage of the special relationship between XML and XSLT

Say we take a Shakespearean sonnet line: “when to the sessions of sweet silent thought.” You’d be crazy to try to try to mark up every word in XML, it’d be a huge undertaking. But XSLT 2.0 can add markup to words using tokenization! Way cool! Tokenized, each word will look like this: <w>word</w> – and here’s a punctuation mark: <nonWord>,</nonWord>

With this markup, XSLT can be used to do a variety of TVAP actions on a text. TokenX ingests XML documents, retaining the original markup, and adds tokens like the examples above. Visualizations include word highlighting, keywords in context (looks a bit like a concordance), replacing words with blocks (for example, to find words that are too long?), highlight punctuation and non-words, all kinds of stuff.

Here’s the TokenX site, if you’d like to play with it.

Analyze, in the TokenX context, means:

– count words in context

– decontextualize words and count them (frex, list all the words in the document alphabetically, or by frequency, each word only once with a number of its occurrences next to it)

– word statistics (how many words, how many elements containing words, mean number of words per element)

– punctuation and non-word statistics

TokenX exports into spreadsheets, so you can export and save your dataset.

You can play with TokenX:

– substitute words

– replace words with images

Best part: it’s free and open-source. “You can change it!” Brian exclaims. Excellent.

Cliff Wulfman is working at Brown – lucky us! (Major shout-out to Cliff.) The full title of his paper is “The Modernist Journals Project: A new architecture.”

Here’s the MJP site. It’s evolved from a quite small-scale faculty project. Cliff talks about how to take one of those and move it toward technologies that will allow it to grow and expand and move at a healthy pace. Their primary-source set is pretty large: modernism grew up largely in periodicals, and they’re digitizing them and putting them online.

Complete runs of magazines are scarce, CW says. Even when they exist, oftentimes the advertising has been stripped.

The MJP started out with a desktop scanner and an OCR (optical-character-recognition) package. One office, one faculty member, several students. That’s all. They tried to digitize all 30 volumes – nearly 18,000 pages! – of The New Age (“a weekly review of politics, literature, and art”) that way.

The limits of the original implementation: it was labor-intensive, hand-scanned and hand-coded; and it was served through eclectic, hand-made HTML pages. The MJP was outgrowing the prot in which it was seeded, CW says. It needed:

– engagement with the concept of “cyberinfrastructure”

– embrace of new technologies, standards, best practices that weren’t in place when the project was first conceived.

So they stepped back and devised a new architecture:

– complex digital objects based on digital library standards (METS, MODS, MADS)

– XML substrate

– data- [?] and database- driven service

– polymorphous delivery: can deliver in formats other than PDF

We then had a demo. Go look at the site for more. :)

Future directions:

– access to new scanner technolgoies will enable vast collection growth

– developing an interlinked encyclopedia of modernism

– build on Fedora‘s digital library infrastructure