</term> , it is extremely likely that they will all share the same <term> sense </term> . This
of these systems , <term> accuracy </term> will always be imperfect . For many reasons
The operation of the <term> system </term> will be explained in depth through browsing
<term> genre </term> . Examples and results will be given for <term> Arabic </term> , but the
assumption that the input <term> text </term> will be in reasonably neat form , e.g. , <term>
expressions </term> , the results of which will be incorporated into a <term> natural language
statistical machine translation tool kit </term> , will be introduced and used to build a working
source code </term> of the <term> tool kit </term> will be made available . In this paper we present
involved in the decision making process will be presented here . <term> Listen-Communicate-Show
cover the basics of <term> SMT </term> : Theory will be put into practice . <term> STTK </term>
target word selection </term> . This paper will concentrate on the second requirement .
results </term> in a short time . The tutorial will cover the basics of <term> SMT </term> : Theory
word dependent substitution costs </term> will demonstrate an additional increase of correlation
information sources </term> . We have built and will demonstrate an application of this approach
aspects of a <term> parse tree </term> that will determine the correct <term> parse </term>
<term> users </term> of our <term> tool </term> will drive a <term> syntax-based decoder </term>
in the <term> sentence </term> , the process will extend to both the left and the right of
<term> free text </term> . The demonstration will focus on how <term> JAVELIN </term> processes
it is actually possible , and after that will lead to predictions of missing <term> fragments
<term> natural language interfaces </term> will never appear cooperative or graceful unless
hide detail