</term> , the <term> error rate </term> dropped to 4.1 % --- a 45 % reduction in <term> error
disambiguation </term> is raised from 46.0 % to 60.62 % by using this novel approach . <term>
and the difference could narrow further to 6.5 % if we disregard the advantage that
<term> parsing accuracy </term> rate from 60 % to 75 % , a 37 % reduction in error . We discuss
<term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre
two <term> formalisms </term> . We then turn to a discussion comparing the <term> linguistic
patterns </term> in a large <term> corpus </term> . To a large extent , these <term> statistics </term>
<term> paraphrase classification </term> and to a lesser extent <term> entailment </term> .
restricted natural language </term> input to a limited-domain computer system . This
information request . The request is passed to a <term> mobile , intelligent agent </term>
Our <term> logical definition </term> leads to a neat relation to <term> categorial grammar
system while gathering information related to a particular scenario . This paper introduces
<term> information workers </term> ' access to a <term> pharmaceutical news archive </term>
with this <term> model </term> is then passed to a <term> phone-string classifier </term> .
</term> from a <term> reconstruction </term> to a <term> recognition task </term> . Implications
Experiments show that this approach is superior to a single <term> decision-tree classifier </term>
. Typically , information that makes it to a <term> summary </term> appears in many different
be both efficient and easily adaptable to a variety of applications . The system
If a <term> computer system </term> wishes to accept <term> natural language input </term>
</term> should be further developed in order to account for the <term> verbal interactions
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