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