LOC Workshop on Etexts by Library of Congress (the reading list .txt) 📕

more markup in the text would facilitate. Thinking about electronic texts as opposed to images means considering material that will never appear in print form, or print will not be its primary form, that is, material which only appears in electronic form. HOCKEY alluded to the history and the need for markup and tagging and electronic text, which was developed through the use of computers in the humanities; as MICHELSON had observed, Father Busa had started in 1949 to prepare the first-ever text on the computer.

HOCKEY remarked several large projects, particularly in Europe, for the compilation of dictionaries, language studies, and language analysis, in which people have built up archives of text and have begun to recognize the need for an encoding format that will be reusable and multifunctional, that can be used not just to print the text, which may be assumed to be a byproduct of what one wants to do, but to structure it inside the computer so that it can be searched, built into a Hypertext system, etc.

******

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

WEIBEL OCLC’s approach to preparing electronic text: retroconversion, keying of texts, more automated ways of developing data Project ADAPT and the CORE Project Intelligent character recognition does not exist Advantages of SGML Data should be free of procedural markup; descriptive markup strongly advocated OCLC’s interface illustrated Storage requirements and costs for putting a lot of information on line

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Stuart WEIBEL, senior research scientist, Online Computer Library Center, Inc. (OCLC), described OCLC’s approach to preparing electronic text. He argued that the electronic world into which we are moving must accommodate not only the future but the past as well, and to some degree even the present. Thus, starting out at one end with retroconversion and keying of texts, one would like to move toward much more automated ways of developing data.

For example, Project ADAPT had to do with automatically converting document images into a structured document database with OCR text as indexing and also a little bit of automatic formatting and tagging of that text. The CORE project hosted by Cornell University, Bellcore, OCLC, the American Chemical Society, and Chemical Abstracts, constitutes WEIBEL’s principal concern at the moment. This project is an example of converting text for which one already has a machine-readable version into a format more suitable for electronic delivery and database searching. (Since Michael LESK had previously described CORE, WEIBEL would say little concerning it.) Borrowing a chemical phrase, de novo synthesis, WEIBEL cited the Online Journal of Current Clinical Trials as an example of de novo electronic publishing, that is, a form in which the primary form of the information is electronic.

Project ADAPT, then, which OCLC completed a couple of years ago and in fact is about to resume, is a model in which one takes page images either in paper or microfilm and converts them automatically to a searchable electronic database, either on-line or local. The operating assumption is that accepting some blemishes in the data, especially for retroconversion of materials, will make it possible to accomplish more. Not enough money is available to support perfect conversion.

WEIBEL related several steps taken to perform image preprocessing (processing on the image before performing optical character recognition), as well as image postprocessing. He denied the existence of intelligent character recognition and asserted that what is wanted is page recognition, which is a long way off. OCLC has experimented with merging of multiple optical character recognition systems that will reduce errors from an unacceptable rate of 5 characters out of every l,000 to an unacceptable rate of 2 characters out of every l,000, but it is not good enough. It will never be perfect.

Concerning the CORE Project, WEIBEL observed that Bellcore is taking the topography files, extracting the page images, and converting those topography files to SGML markup. LESK hands that data off to OCLC, which builds that data into a Newton database, the same system that underlies the on-line system in virtually all of the reference products at OCLC. The long-term goal is to make the systems interoperable so that not just Bellcore’s system and OCLC’s system can access this data, but other systems can as well, and the key to that is the Z39.50 common command language and the full-text extension. Z39.50 is fine for MARC records, but is not enough to do it for full text (that is, make full texts interoperable).

WEIBEL next outlined the critical role of SGML for a variety of purposes, for example, as noted by HOCKEY, in the world of extremely large databases, using highly structured data to perform field searches. WEIBEL argued that by building the structure of the data in (i.e., the structure of the data originally on a printed page), it becomes easy to look at a journal article even if one cannot read the characters and know where the title or author is, or what the sections of that document would be. OCLC wants to make that structure explicit in the database, because it will be important for retrieval purposes.

The second big advantage of SGML is that it gives one the ability to build structure into the database that can be used for display purposes without contaminating the data with instructions about how to format things. The distinction lies between procedural markup, which tells one where to put dots on the page, and descriptive markup, which describes the elements of a document.

WEIBEL believes that there should be no procedural markup in the data at all, that the data should be completely unsullied by information about italics or boldness. That should be left up to the display device, whether that display device is a page printer or a screen display device. By keeping one’s database free of that kind of contamination, one can make decisions down the road, for example, reorganize the data in ways that are not cramped by built-in notions of what should be italic and what should be bold. WEIBEL strongly advocated descriptive markup. As an example, he illustrated the index structure in the CORE data. With subsequent illustrated examples of markup, WEIBEL acknowledged the common complaint that SGML is hard to read in its native form, although markup decreases considerably once one gets into the body. Without the markup, however, one would not have the structure in the data. One can pass markup through a LaTeX processor and convert it relatively easily to a printed version of the document.

WEIBEL next illustrated an extremely cluttered screen dump of OCLC’s system, in order to show as much as possible the inherent capability on the screen. (He noted parenthetically that he had become a supporter of X-Windows as a result of the progress of the CORE Project.) WEIBEL also illustrated the two major parts of the interface: l) a control box that allows one to generate lists of items, which resembles a small table of contents based on key words one wishes to search, and 2) a document viewer, which is a separate process in and of itself. He demonstrated how to follow links through the electronic database simply by selecting the appropriate button and bringing them up. He also noted problems that remain to be accommodated in the interface (e.g., as pointed out by LESK, what happens when users do not click on the icon for the figure).

Given the constraints of time, WEIBEL omitted a large number of ancillary items in order to say a few words concerning storage requirements and what will be required to put a lot of things on line. Since it is extremely expensive to reconvert all of this data, especially if it is just in paper form (and even if it is in electronic form in typesetting tapes), he advocated building journals electronically from the start. In that case, if one only has text graphics and indexing (which is all that one needs with de novo electronic publishing, because there is no need to go back and look at bit-maps of pages), one can get 10,000 journals of full text, or almost 6 million pages per year. These pages can be put in approximately 135 gigabytes of storage, which is not all that much, WEIBEL said. For twenty years, something less than three terabytes would be required. WEIBEL calculated the costs of storing this information as follows: If a gigabyte costs approximately $1,000, then a terabyte costs approximately $1 million to buy in terms of hardware. One also needs a building to put it in and a staff like OCLC to handle that information. So, to support a terabyte, multiply by five, which gives $5 million per year for a supported terabyte of data.

******

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

DISCUSSION Tapes saved by ACS are the typography files originally supporting publication of the journal Cost of building tagged text into the database *

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

During the question-and-answer period that followed WEIBEL’s presentation, these clarifications emerged. The tapes saved by the American Chemical Society are the typography files that originally supported the publication of the journal. Although they are not tagged in SGML, they are tagged in very fine detail. Every single sentence is marked, all the registry numbers, all the publications issues, dates, and volumes. No cost figures on tagging material on a per-megabyte basis were available. Because ACS’s typesetting system runs from tagged text, there is no extra cost per article. It was unknown what it costs ACS to keyboard the tagged text rather than just keyboard the text in the cheapest process. In other words, since one intends to publish things and will need to build tagged text into a typography system in any case, if one does that in such a way that it can drive not only typography but an electronic system (which is what ACS intends to do—move to SGML publishing), the marginal cost is zero. The marginal cost represents the cost of building tagged text into the database, which is small.

******

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

SPERBERG-McQUEEN Distinction between texts and computers Implications of recognizing that all representation is encoding Dealing with complicated representations of text entails the need for a grammar of documents Variety of forms of formal grammars Text as a bit-mapped image does not represent a serious attempt to represent text in electronic form SGML, the TEI, document-type declarations, and the reusability and longevity of data TEI conformance explicitly allows extension or modification of the TEI tag set Administrative background of the TEI Several design goals for the TEI tag set An absolutely fixed requirement of the TEI Guidelines Challenges the TEI has attempted to face Good texts not beyond economic feasibility The issue of reproducibility or processability The issue of mages as simulacra for the text redux One’s model of text determines what one’s software can do with a text and has economic consequences

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Prior to speaking about SGML and markup, Michael SPERBERG-McQUEEN, editor, Text Encoding Initiative (TEI), University of Illinois-Chicago, first drew a distinction between texts and computers: Texts are abstract cultural and linguistic objects while computers are complicated physical devices, he said. Abstract objects cannot be placed inside physical devices; with computers one can only represent text and act upon those representations.

The recognition that all representation is encoding, SPERBERG-McQUEEN argued, leads to the recognition of two things: 1) The topic description for this session is slightly misleading, because there can be no discussion of pros and cons of text-coding unless what one means is pros and cons of working with text with computers. 2) No text can be represented in a computer without some sort of encoding; images are one way of encoding text, ASCII is another, SGML yet another. There is no encoding without some information loss, that is, there is no perfect reproduction of a text that allows