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Markov Models (MEMM) 82 and outperformed previous basel<strong>in</strong>e methods. “We hope to use this
comb<strong>in</strong>ed model for preannotation <strong>in</strong> an active learn<strong>in</strong>g sett<strong>in</strong>g to aid annotators <strong>in</strong> label<strong>in</strong>g a large
Syriac corpus,” wrote McClanahan et al., conclud<strong>in</strong>g that “this corpus will conta<strong>in</strong> data spann<strong>in</strong>g
multiple centuries and a variety of authors and genres. Future work will require address<strong>in</strong>g issues
encountered <strong>in</strong> this corpus. In addition, there is much to do <strong>in</strong> gett<strong>in</strong>g the overall tag accuracy closer to
the accuracy of <strong>in</strong>dividual decisions.” 83
Another challenge faced <strong>in</strong> build<strong>in</strong>g NLP tools for Syriac is that it is an abjad writ<strong>in</strong>g system that omits
vowels and other diacritics, yet the automatic addition of diacritics to Syriac text, accord<strong>in</strong>g to Haertel
et al. (2010), could greatly enhance the utility of these texts for historical and l<strong>in</strong>guistic research. They
consequently developed an automatic-diacritization system that utilized conditional Markov models
(CMMS) and a number of already-diacritized texts as tra<strong>in</strong><strong>in</strong>g data. When test<strong>in</strong>g their system with
related Semitic languages (e.g., Hebrew) as well as resource-rich languages such as English, they
reported that their system outperformed other low-resource approaches and achieved nearly state-ofthe-art
results when compared with resource-rich systems.
The TURGAMA Project, 84 based at the Institute for Religious Studies at Leiden University, is also
explor<strong>in</strong>g the use of “computer-assisted l<strong>in</strong>guistic analysis” for both Aramaic and Syriac translations of
the Bible. In contrast to creat<strong>in</strong>g a tagged corpus, Project Director Wido van Peursen has expla<strong>in</strong>ed that
the TURGAMA Project has focused on develop<strong>in</strong>g a highly specific model of encod<strong>in</strong>g for their
research. He noted that their project started at one level below that of the Oxford-BYU project, or with
the level of morphology rather than POS tagg<strong>in</strong>g.
Van Peursen outl<strong>in</strong>ed a number of challenges that arise when conduct<strong>in</strong>g l<strong>in</strong>guistics analysis with
ancient texts such as Syriac Biblical manuscripts, <strong>in</strong>clud<strong>in</strong>g that there are no native speakers of the
languages <strong>in</strong>volved, there are only written sources, there are multiple manuscript witnesses for
<strong>in</strong>dividual “texts,” and the corpora are typically quite small. These challenges, accord<strong>in</strong>g to van
Peursen, lead to two concrete dilemmas when develop<strong>in</strong>g ancient text corpora: (1) Should
computational analysis be “data-oriented or theory-driven” and (2) What is the priority for the corpus
of the language The analysis of these dilemmas led van Peursen to argue that an encod<strong>in</strong>g model,
rather than a tagg<strong>in</strong>g approach, should be taken:
The challenges and dilemmas mentioned above require a model that is deductive rather than
<strong>in</strong>ductive; that goes from form (the concrete textual data) to function (the categories that we do
not know a priori); that entails register<strong>in</strong>g the distribution of l<strong>in</strong>guistic elements, rather than
merely add<strong>in</strong>g functional labels—<strong>in</strong> other words, that <strong>in</strong>volves encod<strong>in</strong>g rather than tagg<strong>in</strong>g;
that registers both the paradigmatic forms and their realizations; that allows grammatical
categories and formal descriptions to be redef<strong>in</strong>ed on the basis of corpus analysis; and that
<strong>in</strong>volves <strong>in</strong>teractive analytical procedures, which are needed for the level of accuracy we aim
for (van Peursen 2009).
Consequently, the TURGAMA Project’s l<strong>in</strong>guistic analysis of Hebrew and Syriac is conducted from
the bottom up; i.e., at the levels of <strong>in</strong>dividual words, phrases, clauses, and the entire text. The
workflow of their system <strong>in</strong>volves pattern-recognition programs, “language-specific auxiliary files”
82 http://en.wikipedia.org/wiki/Maximum_entropy_Markov_model
83 An earlier discussion of the complicated process of comb<strong>in</strong><strong>in</strong>g mach<strong>in</strong>e learn<strong>in</strong>g, human annotation and ancient corpus creation by this project can be
found <strong>in</strong> Carroll et al. (2007).
84 The full project name is TURGAMA, “Computer -Assisted Analysis of the Peshitta and the Targum: Text, Language and Interpretation.”
http://www.hum.leiden.edu/religion/research/research-programmes/antiquity/turgama.html