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

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and these authors noted that even more <strong>in</strong>terest<strong>in</strong>g results could be ga<strong>in</strong>ed by us<strong>in</strong>g such techniques
with the large corpus of Lat<strong>in</strong> that is grow<strong>in</strong>g onl<strong>in</strong>e, from multiple editions of classical Lat<strong>in</strong> authors
to neo-Lat<strong>in</strong> texts. In a larger corpus, a dynamic lexicon could be used to explore how classical Lat<strong>in</strong>
authors such as Caesar and Ovid used words differently, or the use of a word could be compared
between classical and neo-Lat<strong>in</strong> texts. Another advantage of a dynamic lexicon is that rather than
present<strong>in</strong>g several highly illustrative examples of word usage (as is done with the Cambridge Greek
English Lexicon), it can present as many examples as are found <strong>in</strong> the corpus. F<strong>in</strong>ally, the fact that the
dynamic lexicon supports the ability to search across Lat<strong>in</strong> and Greeks text us<strong>in</strong>g English translations
of Greek and Lat<strong>in</strong> words is a “close approximation to real cross-language <strong>in</strong>formation retrieval.”
Perhaps most important, Bamman and Crane argue that their work to create a dynamic lexicon
illustrates how even small structured-knowledge sources can be used to m<strong>in</strong>e <strong>in</strong>terest<strong>in</strong>g patterns from
larger collections:
The application of structured knowledge to much larger but unstructured collections addresses
a gap left by the massive digitization efforts of groups such as Google and the Open Content
Alliance (OCA). While these large projects are creat<strong>in</strong>g truly million- book collections, the
services they provide are general (e.g., key term extraction, named entity analysis, related
works) and reflect the wide array of texts and languages they conta<strong>in</strong>. By apply<strong>in</strong>g the language
specific knowledge of experts (as encoded <strong>in</strong> our treebank), we are able to create more specific
services to complement these general ones already <strong>in</strong> place. In creat<strong>in</strong>g a dynamic lexicon built
from the <strong>in</strong>tersection of a 3.5 million word corpus and a 30,457 word treebank, we are
highlight<strong>in</strong>g the immense role than even very small structured knowledge sources can play
(Bamman and Crane 2008).
The authors also observed that s<strong>in</strong>ce many of the technologies used to build the lexicon, such as wordsense
disambiguation and syntactic pars<strong>in</strong>g, are modular, any separate improvements made to these
algorithms could be <strong>in</strong>corporated back <strong>in</strong>to the lexicon. Similarly, as tagg<strong>in</strong>g and pars<strong>in</strong>g accuracy
improve with the size of a corpus and as the tra<strong>in</strong><strong>in</strong>g corpus of Lat<strong>in</strong> grows, so will the treebank. In
addition, this work illustrates how small-doma<strong>in</strong> tools might be repurposed to work with larger
collections.
Bamman and Crane (2009) have <strong>in</strong>vestigated these issues further <strong>in</strong> their overview of computational
l<strong>in</strong>guistics and lexicography. They noted that while the TLG and Perseus provide “dirty results,” or the
ability to f<strong>in</strong>d all the <strong>in</strong>stances of a lemma <strong>in</strong> their collections, the TLL gives a smaller subset of
impeccably precise results. Bamman and Crane argued that <strong>in</strong> the future, a comb<strong>in</strong>ation of these two
approaches will be necessary, and lexicography will need to utilize both mach<strong>in</strong>e-learn<strong>in</strong>g techniques
that learn from large textual collections and the knowledge and labor <strong>in</strong>vested <strong>in</strong> handcrafted lexicons
to help such techniques learn. The authors also noted that new lexicons built for a classical
cyber<strong>in</strong>frastructure would need to support new levels of research:
Manual lexicography has produced fantastic results for Classical languages, but as we design a
cyber<strong>in</strong>frastructure for Classics <strong>in</strong> the future, our aim must be to build a scaffold<strong>in</strong>g that is
essentially enabl<strong>in</strong>g: it must not only make historical languages more accessible on a functional
level, but <strong>in</strong>tellectually as well; it must give students the resources they need to understand a
text while also provid<strong>in</strong>g scholars the tools to <strong>in</strong>teract with it <strong>in</strong> whatever ways they see fit
(Bamman and Crane 2009).