Rome Wasn't Digitized in a Day - Council on Library and Information ...

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different apparatuses. In Boschetti’s system, names had to “match items of a table that conta<strong>in</strong>s the
canonical form of the name, abbreviations, orthographical variants and possible decl<strong>in</strong>ations”
The next major step was to develop a set of heuristics to be used <strong>in</strong> automatically pars<strong>in</strong>g the different
apparatuses. Each item <strong>in</strong> the apparatus was separated by a new l<strong>in</strong>e and all items were then tokenized
<strong>in</strong>to one of the follow<strong>in</strong>g categories: verse number, Greek word, Greek punctuation mark, metrical
sign, Lat<strong>in</strong> word, Lat<strong>in</strong> punctuation mark, scholar name, manuscript abridgment, and bibliographic
reference. All scholars’ names, manuscript abridgments, and bibliographic references were compared
with <strong>in</strong>formation from the tables created <strong>in</strong> the previous step. The rest of the tokens were then
aggregated to identify verse references, read<strong>in</strong>gs, and sources. The f<strong>in</strong>al step was the use of an
alignment algorithm to parse text substitutions “<strong>in</strong> order to map the read<strong>in</strong>gs on the exact position of
the verse <strong>in</strong> the reference edition.” Boschetti revealed that about 90 percent of read<strong>in</strong>gs found <strong>in</strong>
apparatuses were substitutions, or chunks of text that should replace one or more l<strong>in</strong>es <strong>in</strong> a reference
edition. His algorithm utilized the concept of “edit distance” 141 to align read<strong>in</strong>gs from the apparatus
with the portion of text <strong>in</strong> the reference edition where the edit distance was lowest. Boschetti also
chose to use a “brute force” comb<strong>in</strong>atorial algorithm that “reconstructs all the comb<strong>in</strong>ations of adjacent
words <strong>in</strong> the reference text (capitalised and without spaces) and it compares them with the read<strong>in</strong>g and
its permutations.” One limitation of his work, Boschetti reported, was that the current system is applied
only to “items constituted by Greek sequences, immediately followed by source,” and excludes those
cases where items <strong>in</strong>cluded Lat<strong>in</strong> language explanations of textual operations to perform.
To test his system, Boschetti calculated its performance aga<strong>in</strong>st 56 verses of Weckle<strong>in</strong>’s edition of
Aeschylus’ Persae and evaluated it by hand. For processed items (exclud<strong>in</strong>g items with Lat<strong>in</strong>
predicates), 88 percent of conjectures were mapped onto the reference text correctly, and 77 percent of
conjectures were mapped correctly <strong>in</strong> the total collection. This work illustrates that although an
automated system does require a fair amount of prelim<strong>in</strong>ary manual analysis, the heuristics and
algorithms that were created provide encourag<strong>in</strong>g results that deserve further exploration.
Recent work by Schmidt and Colomb (2009) has taken a different approach to the challenge of textual
variation, one that also addresses related issues with overlapp<strong>in</strong>g hierarchies <strong>in</strong> markup. Accord<strong>in</strong>g to
Schmidt and Colomb, there are two basic forms of textual variation: that found <strong>in</strong> multiple copies of a
work, such as <strong>in</strong> the case of multiple manuscripts; and that aris<strong>in</strong>g from physical alterations <strong>in</strong>troduced
by an author or copyist <strong>in</strong> a s<strong>in</strong>gle manuscript. Early pr<strong>in</strong>ted books and handwritten medieval
manuscripts often have high levels of variation, and the techniques of textual criticism grew up around
the desire to create a s<strong>in</strong>gle, def<strong>in</strong>itive text. Despite the fact that the digital environment provided new
possibilities for represent<strong>in</strong>g multiple versions of a text, significant disagreement among textual editors
cont<strong>in</strong>ued, as Schmidt and Colomb related:
With the arrival of the digital medium the old arguments gradually gave way to the realisation
that multiple versions could now coexist with<strong>in</strong> the same text. … This raised the prospect of a
s<strong>in</strong>gle model of variation that might at last unite the various strands of text-critical theory.
However, so far no generally accepted technique of how to achieve this has been developed.
This failure perhaps underlies the commonly held belief among humanists that any
computational model of a text is necessarily temporary, subjective and imperfect (Schmidt and
Colomb 2009).
141 Edit distance has been def<strong>in</strong>ed as a “str<strong>in</strong>g distance,” or the number of operations required to transform one str<strong>in</strong>g <strong>in</strong>to another (with typical allowable
operations, <strong>in</strong>clud<strong>in</strong>g the <strong>in</strong>sertion, deletion, or substitution of a s<strong>in</strong>gle character) (http://en.wikipedia.org/wiki/Edit_distance).