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    pdftitle={Creating Tools for Morphological Analysis of Sumerian},
    pdfauthor={Valentin Tablan, Wim Peters, Diana Maynard},
    pdfsubject={Morphological Analysis, Sumerian},
    pdfkeywords={Morphological Analysis, Sumerian},
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\title{Creating Tools for Morphological Analysis of Sumerian}

\author{Valentin Tablan, Wim Peters, Diana Maynard, Hamish Cunningham} 

\name{Valentin Tablan, Wim Peters, Diana Maynard, Hamish Cunningham} 

\address{Department of Computer Science\\
University of Sheffield\\
Regent Court, 211 Portobello Street\\
S1 4DP, Sheffield, UK\\
\{valyt, wim, diana, hamish\}@dcs.shef.ac.uk}


\abstract{
Sumerian is a long-extinct language documented throughout the ancient Middle
East, arguably the first language for which we have written evidence, and is a language isolate (i.e. no related languages have so far been identified). The Electronic Text Corpus of Sumerian Literature (ETCSL), based at the
University of Oxford, aims to make accessible on the web over 350 literary works
composed during the late third and early second millennia BCE. The transliterations and translations can be searched, browsed and read online using the tools of the website. In this paper we describe the creation of linguistic analysis and corpus search tools for Sumerian, as part of the development of the ETCSL. This is designed to enable Sumerian scholars, students and interested laymen to analyse the texts online and electronically, and to further knowledge about the language.}

\begin{document}

\maketitleabstract


\section{Introduction}

The Sumerian language of ancient Sumer is a long-extinct language documented throughout the ancient Middle
East, in particular in the south of modern Iraq, from at least the 4th millenium BC. It is arguably the first
language for which we have written evidence, the rival candidate being ancient
Egyptian. Sumerian was replaced by Akkadian as a spoken language around 2000 BC, but continued to be used as a sacred, ceremonial and scientific language in Mesopotamia until about 1 AD \cite{wikipedia:sumerian}.

The Electronic Text Corpus of Sumerian Literature (ETCSL), based at the
University of Oxford, aims to make accessible on the web over 350 literary works
composed during the late third and early second millennia BCE. The corpus
comprises Sumerian texts in transliteration, English prose translations and
bibliographical information for each composition. The transliterations and the
translations can be searched, browsed and read online using the tools of the
website.

In this paper we describe the creation of linguistic analysis and corpus search
tools for Sumerian, as part of the development of the ETCSL. This is designed to
enable Sumerian specialists to analyse the texts online and electronically and
to further knowledge about the language.

The benefit of having linguistic information as part of cultural heritage digital libraries has been demonstrated by the success of the Perseus Digital Library \footnote{http://www.perseus.tufts.edu/}, where this information is used not only by scholars, but also by students and interdisciplinary researchers who want to study the electronic collections but are not proficient in the language \cite{crane96}. In a similar fashion, we expect that the creation of tools for Sumerian is likely to enhance the accessibility of this collection to scholars, students and the general public. 


%Wim's section
\input{morphology}


%Valy's section
\input{application}


\section{Evaluation of Results}

To evaluate the results, we obtained a copy of the corpus automatically
annotated with morphological information using a tool developed at the
University of Pennsylvania\footnote{http://psd.museum.upenn.edu/epsd/index.html}. Although that annotation is not perfect (the tool
does make some mistakes and also the model of Sumerian morphology used differs
slightly from the one defined by the Oxford group) it does give us a good
indication of where problems might occur. In the current development state of
the application, the results as evaluated over a document containing some 2300
nouns and 1400 verbs are as follows: 

\begin{table}[htb]
\begin{center}
\begin{tabular}{|l|l|l|l|}
\hline
Type & P & R & F \\
\hline
noun recognition & 59\% & 84\% & 69\% \\
verb recognition & 65\% & 67\% & 66\% \\
morphological analysis &  52\% & 73\% & 61\% \\
\hline
\end{tabular}
\caption{Evaluation of POS recognition and morphological analysis}
\label{table:evaluation}
\end{center}
\end{table}



The only other system for automatically analysing Sumerian that we know of is
the work at Pennsylvania which we are using as the gold standard. So we can only
compare our work with this. A manually created gold standard is forthcoming from ETCSL. 

Considering the difficulty of the task and
the stage of the work, these results are very promising. We have not yet
measured the morphological analysis of verbs but this is forthcoming. Note also
that since there is much ambiguity between nouns and verbs, errors in the
identification of nouns will generally also have an impact on identification of
verbs, and vice versa, because missing nouns will often be falsely identified as
verbs and so on.
 
 

 \section{Corpus Search Tools}
 

 
\begin{figure*}[htb]
\begin{center}
\includegraphics[scale=0.375]{../../images/annic_sumerian1.png}
\caption{Using ANNIC on Sumerian text}
\label{fig:annic_sumerian1}
\end{center}
\end{figure*}

 The linguistic analysis tools described above are complemented by the
 development of a tool for advanced search and visualisation of linguistic
 information, ANNIC (ANNotations In Context) \cite{Aswani05}. This provides an alternative
 method of searching the textual data in the corpus, by identifying patterns in
 the corpus that are defined both in terms of the textual information (i.e. the
 actual content) and of metadata (i.e. linguistic annotation and XML/TEI
 markup). 
ANNIC is similar to a KWIC (KeyWords In Context) index, but where a KWIC index provides simply text in context in response to a search for specific words, ANNIC additionally provides linguistic information (or other annotations) in context, in response to a search for particular linguistic patterns. ANNIC is based on Jakarta Lucene, but extends the model to index large corpora and allow users to query on annotations and their features by providing JAPE patterns (LHS rules), as described in Section \ref{sec:application}. This enables the retrieval of information in the form of Annotations in Context, and provides a useful interface to assist the creation of new JAPE rules. The functionality is provided as a plugin in GATE. 

ANNIC consists of two processing resources (index and search) and a visual
resource (viewer). The index processing resource creates an index that is
required for the search process. A corpus can be indexed on words, morphs or
other annotations as appropriate -- these are the segments that will be searched
on in the second stage. The search processing resource takes as input a pattern
on which to search, which can consist of annotations and regular expressions: for
example, one can search on specific combinations of morphs or whole words. A
context size parameter is also set, determining how large a context window
should be used. The viewer is the interface which displays the results. Given a
query to the ANNIC Search engine, it returns the list of documents that contain the
specific pattern, and for each document it returns the patterns and contexts.
Users have the option of viewing the results in different ways according to
their needs. Figure \ref{fig:annic_sumerian1} shows a screenshot of ANNIC with Sumerian text.



\section{Conclusions}

In this paper we have described the development of tools for the linguistic
analysis of Sumerian, including a facility to search a corpus of annotated text
for morphological patterns. Work is still ongoing but current results are very
promising. The complexity of Sumerian and the scarcity of available textual material will, however, remain obstacles for the success of automatic analysis.

The additional search facilities embodied in the ANNIC tool will enable the user to perform manual checks and detect additional combinatorial phenomena in the text corpus.



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