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{\bf Issue Statement for Digital Tool Summit in Linguistics}\\[6pt]
{\bf Lori Levin}\\
{\bf Language Technologies Institute, Carnegie Mellon University}

\paragraph{Issue: We need fast ways to create tools for more languages:}
Treebanks (corpora annotated with phrase structure or
dependency structure trees) have been used as research tools in
computational linguistics since the 1990s.  Many treebanks include
search tools.  For example, TGREP and TGREP2 allow users to describe
tree templates and then find treebank examples that match the
templates.  Such searches are valuable for studying many syntactic
phenomena including coordinate structures, subcategorization patterns,
long distance dependencies, and many kinds of special constructions.
Treebanks, however, are of limited size.  More recently, robust
parsers have become available for some languages.  These parsers can
be used to parse large corpora (anything that can be found on the Web)
with reasonable accuracy, thus expanding the amount of annotated text
that can be searched.  Unfortunately, treebanks and/or robust parsers
are available for only about a dozen languages (English, Chinese,
Dutch, Arabic, Czech, and a few others).

\begin{enumerate}
\item {\bf What are the most pressing needs among possible cyberinfrastructure 
and/or digital tools for linguistics?}  Basic tools for more
languages, including morphological analyzers, lexicons, corpora,
annotated corpora and parsers.  A broader range of corpus annotations
(syntactic, semantic, and pragmatic) is also needed.  

\item {\bf What are some enduring challenges in creating
cyberinfrastructure and/or digital tools for linguistics?}  The time
consuming process of adapting parsers and annotating corpora for new
languages.  The challenge is to develop tools that will partially
automate and speed up the process of developing resources for new
languages.

\item {\bf Which existing resources can be leveraged to create digital tools 
for linguistics?}  Linguists should learn the capabilities of machine
learning techniques for automatically learning grammars and other
linguistic structure.  Machine learning techniques for NLP seem
initially repugnant because many of them are lingusitics-free.
However, they produce analyses of data that can support linguistic
research.  Linguistis don't need to understand how they work, just
what they can do for you.  They can speed up the creation of resources
for new languages.  

\item {\bf How can documentation tools make language resources (e.g. text, 
lexical or morphological corpora) more readily available for 
historical, typological, and other theoretical analyses?}
Annotated corpora are obviously useful, but
computational linguists should build easy-to-use interfaces for
non-computational linguists.  A good example is Philip Resnik's
Linguist's Search Engine.  
\end{enumerate}


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\paragraph{Biography}

\noindent
Lori Levin is an Associate Research Professor in the Language
Technologies Institute in the School of Computer Science at Carnegie
Mellon University.  She received a B.A. degree in Linguistics at the
University of Pennsylvania in 1979 and a Ph.D. in Linguistics from MIT
in 1986.  Her thesis research was on the lexical mapping theory of
Lexical Functional Grammar.  After teaching Linguistics at the
University of Pittsburgh from 1983 to 1988, Levin took a job at CMU in
the Center for Machine Translation, which later became the Language
Technologies Institute.  Since 1988, she has conducted research on Machine
Translation of spoken and written language, information extraction
from email, and computer assisted language learning.  She has supervised
projects on at least 12 languages including European, Asian, Semitic,
and Native American languages.  

In 2001 Levin became a co-PI of an NSF ITR project, AVENUE along with
Jaime Carbonell (PI) and Alon Lavie (co-PI).  AVENUE automatically
learns rules for machine translation from small parallel corpora.
AVENUE includes a set of tools that are useful outside of the field of
machine translation including an ontology of functional/communicative
meanings and an elicitation tool that allows informants to translate
sentences and align words.  
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