Category: digital [humanities|libraries]

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

David Hirtle is doing graduate work here at the University of New Brunswick, in computer science. The full title of his paper is “TRANSLATOR: a TRANSlator from LAnguage TO Rules.”

Semantic web is still n ot widely used.

– Focus of current development: machine-readable (meta)data

– Problem: only experts can contribute. Need to lower barrier to entry.

Provide a user-friendly format!

– why not English [he really means natural language]?

– “controlled English” avoids ambiguity: it’s formal, but also natural

TRANSLATOR will translate “every student gets a discount of 15 percent” to express [in XML, from what I see] that “student” implies “customer,” etc.

ACE (attempto controlled english):

– looks like English: “every honest student who does not procrastinate receives a good mark and easily passes the course.”

– but actually a formal language, like RDF: a tractable of English – all ACE sentences are English, but not vice versa

– every ACE sentence can be unambiguously translated into logic.

Strategies for handling ambiguity:

– exclude imprecise phrasings (“students hate annoying professors” – do they hate to annoy profs, or do they hate profs who are annoying?)

– interpretation rules (“the student brings a friend who is an alumnus and receives a discount” – who receives the discount? in ace, by default, it’s the student because of a certain rule. If you want it to be the alumnus, you write “…and who receives a discount.”)

How can rules be expressed?

– in natural language, many different forms (everyone is mortal, all humanity is mortal, for each person the person is mortal)

– all above are valid ACE

– further embellishment (negation, relative clauses, etc) [vz: but doesn’t that add ambiguity?]

What can’t yet be easily expressed?

– “infix” implication (“the student is happy if there is no class” – solution: TRANSLATOR swaps the condition(s) and conclusion(s) and voila, ACE-acceptable)

– production and reaction rules (involve actions: “if a student is caught cheating then send a report to the registrar” requires the imperative mood, which is not yet in ACE)

Discourse representation structures, and more technical info. Sad, I can’t reproduce his diagrams here. The rules are eventually translated into RuleML, in whose development David is participating.

RuleML:

– goal is interoperable rule markup (XSLT translators to other semantic web languages)

– family of “sublanguages” (modular XML schemas; each represents a well-known rule system; TRANSLATOR uses First-Order Logic sublanguage)

Why use RuleML?

– ease of interchange (XML)

– compatibility with RDF and other languages, as well as W3C’s upcoming Rule Interchange Format

– availability of tools

– wide fariety of features (negation-as-failure, weightings, data types etc.)

Again, work-in-progress. Truly an attempt at getting closer to the semantic web. Formalizing natural language, what a gargantuan task. One critical benefit of TRANSLATOR is that it “allows non-experts to write facts and rules for the semantic web.” When can we play with it?

Now, it appears. Here’s a site for TRANSLATOR, including a Java Web Start demo.

Shelly Lukon and Patrick Juola are both at Duquesne University. The full title of their paper (presented by Lukon) is “Designing a context-sensitive machine-aided index generator.”

Problem definition.

Back-of-the-book indexing provides relevant terms, identifies cross-references and subcategories, and has a static, rigid structure (as opposed to web indexing). Human indexers invest a LOT of time into indexing (1 week per 100 pages of text); use software to automate mundane texts; and make all the intelligent indexing decisions. SL&PJ’s prototype system bridges the gap between the human and currently available tools, but not to replace the human indexers.

They’ve interviewed professional indexers, product-tested some of the software packages they tend to use, and looked at some mathematical techniques (particularly LSA, latent semantic analysis) that have had proven success in text processing and capturing semantic content of terms.

Cognitive tasks involved in index construction:

– identifying terms to index;

– locate all informative references;

– identify/locate synonymous terms;

– split index terms into subterms;

– develop cross-references within text;

– compile page numbers.

Their techniques for obtaining semantic information:

– parsing/tagging of terms, frequency analysis

– LSA

– word sense disambiguation (WSD)

– hierarchical cluster analysis (HCA)

This is still a work in progress. So far they’ve been able to locate all informative terms in text, and to allow the user to set thresholds/parameters. LSA, WSD and HCA show first level of clustering nearly 40% accurate upon inspection (not great, but a solid start). Their single-processor PC takes several hours to process small (60K words) corpora. Better than a human’s speed!

They’re categorizing words into parts of speech: identify the part-of-speech of each term; label each term with delimiter and acronym (home becomes home/NN since home is a noun). They’re only dealing with English right now. Their app is written in Java, as is MontyLingua which they’re using for part-of-speech tagging.

LSA:

-use factor analysis to generate numerical representations of terms and their meanings;

-divide corpus into “documents” (paragraphs), then analyze each unique “term” (word) relative to each document;

-create term-by-document matrix;

– create term-by-term covariance matrix (look at how each pair of terms vary together)

– singular value decomposition (SVD) – a way of explaining variability among random variables (dimensions)

– decompose covariance matrix into three submatrices [over my head here]

– rank resulting values

– reconstruct using most significant dimensions (reduce noise, sharpen similarities/contrasts)

– 200 most significant dimensions: pinpoint each term’s location in 200-dimension “semantic space” [why 200?]

WSA

– separate out different senses (meanings) of each term token

– numerical encodings generated by LSA give average context for each term token

– look at encodings of the other terms surrounding each occurrence of the token

– Example: the word “bass” occurs throughout text (both as fish and as musical instrument), proximate to other words (guitar, boat, fish) that help disambiguate

– disambiguate “bass” into “bass_fish” and “bass_instrument”

HCA

– partition terms into subsets with similar properties/characteristics

– antonyms as well as synonyms will cluster together (both have strong relationships, but the system doesn’t know whether they’re positive or negative)

– this information can be used to identify cross-refs (see also) and subterms

This is a machine-aided system. Its purpose is not to replace but to assist the human indexer, whose judgment and experience cannot be fully captured by a sophisticated expert system. Users can edit results at any stage, control indexing parameters, etc.

Metrics for evaluating the “goodness” of the resulting index:

– side-by-side comparison between entirely-human-generated and machine-aided indexes of the same dataset, quantify what percentage of agreement is acceptable, maybe find meaningful information in how they disagree as well.

Future work:

– incremental refinement

– system has modular architecture for ease of swapping out individual components

– need robust, effective user interface

– empirically vary frequency thresholds, weighting methods, number/percentage of dimensions to use in the reduced data matrix

– continue to build in the latest/most efficient indexing/retrieval methods.

What a great project. I’d love to use it for RolandHT, but it probably won’t be done in time. Enabling the software to read/process XML is on their wish list of big enhancements, hooray!

[Oh look: Geoffrey Rockwell is posting some of his thoughts about this CaSTA conference on the TADA wiki. Highly recommended reading.]

Ian Munro is the Canada Research Chair in Algorithm Design at the Univ. of Waterloo. The full title of his keynote is… well, in the schedule it’s “Computer science research for text analysis,” but on the opening slide it’s “Developing text analysis software.”

Will talk about text search, one of his interests. He’s hard-core CS.

The need for computing in the humanities: “Scholarship increasingly depends on electronic document repositories and the growth of digital libraries… Even more apparent in computer readable form are collections of business documents and linguistic corpora. Gray literature, including technical reports, personal communications, and online help information, also constitute a growing text source.” –Frank Tompa

IM’s resaerch: data structures. How to organize information so we can find what we want: quickly; using an acceptable amount of space; proving the necessary inherent time and space bounds. [vz: bless his heart.] He’s on the theoretical side of computer science, a very different side from “user interface” or “understanding natural language” sides.

Where di IM get going on text? The Oxford English Dictionary project; interaction with humanists and lexicographers. New problems to work on; great data.

Another project IM was involved in, in the early 1980s: Videotext. Like the internet, but assumed few information providers, and access would’ve probably been restricted. The software ideas were there, but it was too early to use them.

IM gives some history of the OED project, which is actually covered pretty well in the Wikipedia article about it. The article includes a description of the first SGML encoding(s) of the OED.

The software they developed for the OED project:

– Lector, a general purpose browser. Worked with tagged text, presented in reasonable form, early SGML that, were they doing this a bit later, would’ve been HTML.

– Goedel, a programming language/database system.

– Pat, a search engine.

Pat is short for PATRICIA, “Practivel Algorithm to Retrieve Information Coded in Alphanumeric.” [vz: oy!] It does full-text searching, using an approach now generally known as “suffix tree.” In fact, in the final implementation it was a “suffix array.”

Typical problem: text indexing. Let’s take a large text file, like all the documents/email for a company, or a genome. We need to construct a structure so that given an arbitrary phrase they can quickly find where this phrase occurs in the “document.” Call the “extra stuff” an index.

What’s “suffix array”? It’s a method: an array of pointers referring to text positions in lexicographic order. Allows binary research. More on it here. (By the way, about this and other links: yeah, it’s wikipedia. Don’t even start with me on it being a Bad Resource. It’s not, unless you take it for gods’ word.)

Then IM describes suffix tries. This is all so far over my head that I’m not even going to try to summarize it; besides, the link does it pretty well.

From the OED project, IM and colleagues’ work proceeded to:

– more text search;

– data warehousing for asking complex queries

– enabling people to view relational databases as text (tags substituted for fields)

– enabling people to get things in “sorted” order: online phone books; buildings wired separately [tell me the companies that have offices in buildings I, a phone company, have wired – but who are not yet customers of mine); Sarah Lee = Sara Li; Romeo and Juliet (how many places are there in England where someone named Romeo lives near someone named Juliet? IM says that the answer is three.)

Where do things go next? IM wants to get rid of the tedium of searching in raw form, scanning texts etc, all parts of humanities work; improve the language interface; utilize better OCR (optical character recognition); build an application that can handle archaic linguistic forms.

Missed the very beginning. Stan Ruecker again; Milena Radzikowska (Mount Royal College); and Stéfan Sinclair (McMaster University). “Communicating process with form: designing the visual morphology of the Nora data mining kernels.” Stan presents the new Nora interface. Which is VERY pretty, but sadly not viewable online (but see the Nora link below).

Rich-prospect interfaces:

– some meaningful representation of every item in the collection

– tools for manipulating the display

– tools shiould rely on information emergent from the collection

– [missed the other three]

Introduces NORA.

Purpose:

– classification and pattern recognition (“find me documents that I’ve identified, and tell me the features that you’re working with.”)

– “specifically, we aim at allowing literary scholars to identify categories of interest in collections of literary texts, and then to find new members of those categories and explore the features that correlate with those categories…” -John Unsworth

“Kernels” are objects that you’re working with. As you group cultural artifacts together and define some criteria for grouping, the kernel’s (initially blank-outline) iconocragphic representation changes, becomes more complex. Each kernel has five different states, from blank to very complex: leaves, snowflakes, concentric circles and other metaphors – they don’t seem to have settled on a single metaphor yet, but they’ve agreed that five states are definitely not enough.

You generally have multiple kernels, with a pallette for each; so you can see a bird’s-eye view of all your kernels. You can also “save” a kernel by dragging it to the “deasktop” environment (this is all happening in your browser), and then you can share that kernel image with others or refer to it in your own research later.

Sort of a Photoshop for text analysts. With much, much easier controls. I’m looking forward to more development of this!

NORA (No One Remembers Acronyms) is merging soon with MONK (Metadata Opens New Knowledge). Soon they’ll all be known as MONKeys!

Last paper of the day – the rest will be posters. see y’all tomorrow.

Terry Butler is at the University of Alberta. The full title of his paper is “Automated indexing using an existing thesaurus: a bridge between Coleridge and Roget.”

The project aims to usefully present a digital edition of Samuel Coleridge’s notebooks. They’ve been printed, with annotations, and published by Princeton. The notebooks are published with name and place indexes, but without a subject index (a big drawback for researchers). Current project is focusing on building an electronic subject index to the print edition.

The complexity of transforming handwritten notebooks into a printed format is staggering. Butler et al. are digitizing the printed edition, and semantically encoding it. The encoding captures not only the structure of the text, but also its dynamic nature – the deletions, insertions etc. so strongly present in Coleridge. Plus a thematic index. They’re also marking up foreign-language material as being in other languages.

Roget’s thesaurus was first published in 1852, and he’d been working on it for 30 years. So the thesaurus is roughly contemporary with Coleridge’s writings, and word similarities imply similarities in thinking and interests between the two men. They want to link up Roget’s broader categories to instances of writing in Coleridge, as a starting point to studying the notebooks.

They take all of the words from each of the notebook entries, stripping out metadata and foreign words, and now they have a “bag of words.” They then stem the words to get more matches between them and the thesaurus. They want to find out connections: which of these words also occur in Roget’s Thesaurus (25K+ words, 1K+ headings, organized into 6 large classes)? Results are displayed in both directions: all the Roget connections to a given note; and all the notes connected to a particular Roget entry.

Value of automatic linking to scholarship:

– provides access for searching that is complementary wto what the text itself says

– text is related to larger, consistent conceptual categories

– it can be developed (from the electronic text) in a few hours’ [of computer processing] time: a custom thesaurus would take hundreds of hours to create.

Limits of Roget:

– it’s a verbal construct: not really about the world, but about words

– it has a lack of proper nouns

– it contains a large quantity of rare, obscure, dated words.

Assessing effectiveness:

– a subset of the results was given to expert team members for review: will this be a useful first step to creating a Coleridge thesaurus?

– links are distributed across information space [vz: ?]

Next steps:

– develop a [complete] customized Coleridge thesaurus;

– attempt to link between Roget and another contemporary text.

Renita Machado and Robert Murimi are in the Dept. of Electrical and Computer Engineering at the NJ Institute of Technology in Newark. The title of their paper, presented by Machado, is “RealBook – Reading a book through the eyes of reality.”

RealBook focuses on the use of wearable computers to convert the experience of reading a book into images and sound. They propose the use of an augmented reality system made up of a head-mounted display [with camera sensor], headphones, a physical book, and RealBook software. The latter is meant to augment the pleasures of reading a book.

They haven’t built it yet, but the most interesting thing they want it to do is convert events in a book images and sounds. They’ll use animation, which they test by performing participatory design (preliminary studies that produce, effectively, storyboards). The software needed will need to display enough animation to convey the meaning/flow of the book.

They want to encourage healthy competition among potential manufacturers of this thing. It could potentially apply not only to fiction but to non-fiction as well – textbooks, for example! Interesting.

The closest resemblance to this work is Magic Book, a tool to view illustrations in books in both augmented reality and virtual reality views. RealBook would be primarily entertainment, though, Machado emphasized.

[vz: sounds fascinating. Sci-fi. I want to play with this thing, and am impatient for it to get built.]