\section{Morphological Analysis Tools for Sumerian}
\label{sec:application}

The main aim of our work is to create a set of tools for performing automatic
morphological analysis of Sumerian. This essentially entails identifying the
part of speech for each word in the corpus (technically, this only involves
nouns and verbs which are the only categories that are inflected), separating
the lemma part from the clitics and assigning a morphological function to each
of the clitics. In order to do this, we used the model of Sumerian morphology
defined by a team of Sumerologists, which we then represented in a way that can
be used for automated language processing. 

The morphological model we used
consists of noun and verb templates comprising a lemma plus a number of
morphological slots that could be filled. The nouns have a lemma and up to six
suffix slots while the verbs have up to twelve prefix slots, a lemma and two
suffix slots. For each slot there is a known list of morphemes that can fill it
and a set of restrictions encoding dependencies between the slots, such as
agreement in gender. The lists of candidate slot fillers have non-null
intersections -- the same morpheme can appear in several lists, though usually
with different functions.

There are two main phases involved in our morphological analysis of transliterated
Sumerian -- a normalisation stage which deals with various surface phenomena which affect the way Sumerian words are
written, such as reduplication or assimilation, and the actual morphological analysis which identifies parts of speech
and assigns functions to the various morphemes.

The implementation for the morphological analysis tools has been done using the
GATE language engineering platform \cite{Cun02b} and includes the following
high-level tasks:

\begin{description}
\item[Tokenisation:] splits the input text into syllables while identifying
special text components such as determiners and markers for damaged regions in
the original clay tablet.
\item[Input normalisation:] makes explicit the ambiguity caused by some
phenomena in written Sumerian by generating all possible normalised 
interpretations for each particular text fragment.
\item[Slot fillers look-up:] identifies syllables in the input that are 
candidate fillers for morphological slots.
\item[Non-inflected words lookup:] identifies words that are not inflected by
looking them up in a predefined list.
\item[Morphological analysis:] identifies nouns and verbs and generates 
structure information by labelling the lemma and all the other constituents.
\end{description}

The tokenisation step is performed by a customised variant of the GATE English
Tokeniser. Its role is to identify syllables -- defined as contiguous sequences
of letters, dashes, determinatives -- marked by square brackets in the
transliteration schema, and entities -- marked by round brackets. The output of
the tokeniser consists of a set of annotations of various types (syllable, dash,
entity, determinative and whitespace) which cover the entire input text.

The output from the tokeniser is then normalised by adding new syllables in
parallel with the existing ones where the surface form can be interpreted in
several different ways. For example, in the word {\tt sizkur2-ra} the second
syllable can either be seen as {\tt ra}, or {\tt a} in the case when the {\tt r}
was a reduplication of the final consonant in the previous syllable, or {\tt ak}
where {\tt r} is a reduplication and the final {\tt k} was dropped. The
normalisation phase will add all possible alternative interpretations for all
syllables where such phenomena occur. This will generate noise (as only one of
all the alternate interpretations is the right one); the intention is to let the
later analysis phase choose the right one based on combinatorial restrictions encoded by the
morphology model.

The next phase in the processing identifies the candidate slot
fillers. This is essentially a list look-up operation based on the pre-compiled
lists of possible fillers for each morphology slot. This was implemented using
the GATE Gazetteer which needed to be modified in order to deal with the
alternative syllable interpretations generated by the tokenisation phase. All
sequences of one or more syllables that could play the role of a morphological
slot are annotated with their type (i.e. part of a noun or a verb), slot
position in the noun or verb template and morphological function.

The following step is that of identifying words that are non-inflected -- another
list look-up operation, based on a list of known words which, although
multi-syllabic, do not show inflection. This was necessary because our analysis
method is based on looking at the internal structure of each word and trying to
identify morphemes that could play the role of clitics. Multi-syllabic
non-inflected words can trick the system when some of the syllables in their 
lemmas could also be interpreted as morphological markers.

The final phase of the analysis process is that of identifying nouns and verbs
and performing morphological analysis determining which syllables form the
lemma and which are clitics with various functions. This was implemented as a
cascade of JAPE transducers \cite{Cun01f}. The first one identifies exceptional
spellings and normalises them -- for instance the {\tt nuc} clitic is sometimes
written as {\tt ni-ic}. After this final normalisation, grammars that identify
nouns and verbs are run. These essentially try to find words that satisfy
regular expression-like patterns based on slot candidate and syllable
annotations created by the previous modules. The word patterns encode both the
correct position of the various slots in relation to the lemma and the other
restrictions to do with various type of agreement (for example one cannot have
both an {\tt animate} and {\tt inanimate} marker as part of the same word). The
assignment of priorities to the various rules is done in such a way that
versions that assign more syllables to morphological functions are preferred to
the ones that assign more syllables to the lemma. This assures that when a
particular syllable can be successfully assigned a morphological function, then
this will be done in preference to considering it part of the lemma.

Although the application was designed to address both nouns and verbs at the
same time, we have concentrated our efforts first on the noun morphology, which
is the simpler case because of the fewer slots, aiming to direct our attention
to the more complex case of verbs after we get a good understanding of the
phenomena we need to address, and when we are confident that the architecture of our
application is well suited for Sumerian morphology. Work is currently in
progress on improving the analysis of the verbs.
