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Another very useful feature of xfst is the ability to create scripts with several
commands in a sequence. The later commands can operate on the output of earlier
commands, and can thus create a cascade of finite-state transducers. Xfst also provides
convenient ways of outputting all the words recognised by a given transducer, which
proved very useful in the creation of the online reference (see section 5). An updated
version of xfst (Beesley & Karttunen forthcoming 2006) also includes support for utf-
8.
While finite-state technology is very good at generating and recognising regular
expressions, it has a harder time capturing other features of natural language such
as non-concatenative morphological structure. The next section describes some
adaptations that allow FST to handle many of the non-concatenative patterns in
Georgian.
In addition, FST is not designed to represent a dynamic, living mental lexicon of
an actual speaker. It does not provide any mechanisms for probabilistic decisions, or
for recognition and generation of novel inflectional forms. The concluding section
discusses some possible future developments in this area.
4. Computational Model of the Georgian Verb
4.1 General Idea
As argued above, Georgian verb morphology can be described as a series of patterns
at various levels of regularity. Most of the patterns specify particular morphosyntactic
or semantic properties of verb forms and the corresponding combinations of elements
in the morphological templates. In the model proposed here, screeve formation is
viewed as lexical or semi-regular, and pronominal agreement is viewed as completely
regular.
Screeve formation for different conjugation classes (transitive, unergative,
unaccusative, and inverse) is fairly different in Georgian, and so each conjugation class
is implemented as a separate network. Nevertheless, the principles for composing
each network are the same.
The model is implemented as a cascade of finite-state transducers, that is, as
several levels of FST networks such that the result of composing a lower-level network
serves as input to a higher-level network. The levels correspond to the division of
templatic patterns into completely lexical (Level 1) and semi-regular (Level 2). Level
3 contains completely regular patterns that apply to the results of both Level 1 and
Level 2. The result of compiling Level 3 patterns is the full set of conjugations for the
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