these <term> evaluation techniques </term> will provide information about both the <term>
information sources </term> . We have built and will demonstrate an application of this approach
<term> free text </term> . The demonstration will focus on how <term> JAVELIN </term> processes
<term> genre </term> . Examples and results will be given for <term> Arabic </term> , but the
of these systems , <term> accuracy </term> will always be imperfect . For many reasons
results </term> in a short time . The tutorial will cover the basics of <term> SMT </term> : Theory
<term> users </term> of our <term> tool </term> will drive a <term> syntax-based decoder </term>
<term> sentences </term> . In this paper , we will present a new <term> evaluation measure </term>
<term> natural language interfaces </term> will never appear cooperative or graceful unless
assumption that the input <term> text </term> will be in reasonably neat form , e.g. , <term>
basics of <term> monolingual UCG </term> , we will show how the two can be integrated , and
expressions </term> , the results of which will be incorporated into a <term> natural language
it is actually possible , and after that will lead to predictions of missing <term> fragments
3-character Chinese names without title </term> . We will show the experimental results for two <term>
aspects of a <term> parse tree </term> that will determine the correct <term> parse </term>
</term> , it is extremely likely that they will all share the same <term> sense </term> . This
target word selection </term> . This paper will concentrate on the second requirement .
hide detail