<term> information retrieval techniques </term> use a <term> histogram </term> of <term> keywords
available on the <term> surface </term> and could be used directly . Despite the small size of the
small size of the <term> databases </term> used some results about the effectiveness of
<term> Communicator </term> participants are using . In this presentation , we describe the
and <term> scenario templates </term> - can be used to enhance access to <term> text collections
</term> . We describe how this information is used in a <term> prototype system </term> designed
elicited from duplicating the experiment using <term> machine translation output </term> .
approach called <term> LCS-Marine </term> . Using <term> LCS-Marine </term> , tactical personnel
<term> word string </term> has been obtained by using a different <term> LM </term> . Actually ,
combiner </term> with <term> hard decisions </term> using the <term> reference </term> . We provide experimental
mimics the behavior of the <term> oracle </term> using a <term> neural network </term> or a <term> decision
component performance </term> . We describe our use of this approach in numerous fielded <term>
top-ranked <term> plan </term> . The <term> SPR </term> uses <term> ranking rules </term> automatically
many attractive properties which may be used in <term> NLP </term> . In particular , <term>
are directed by a <term> guide </term> which uses the <term> shared derivation forest </term>
natural language </term> , current systems use manual or semi-automatic methods to collect
surprisingly close to what can be achieved using conventional <term> word-trigram recognition
<term> unsupervised training </term> is first used to train a <term> phone n-gram model </term>
</term> . The <term> model </term> is designed for use in <term> error correction </term> , with a
selection </term> . Furthermore , we propose the use of standard <term> parser evaluation methods
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