for use in <term> error correction </term> , with a focus on <term> post-processing </term> the
</term> on standard <term> WSD datasets </term> , with promising results . This paper presents
relies upon the <term> actor paradigm </term> , with <term> concurrency </term> entering through
</term> for each input <term> sentence </term> , with associated <term> probabilities </term> that
<term> system </term> is state-of-the-art , with guaranteed <term> grammaticality </term> of
and <term> associated event elements </term> . With independent approach , we identify important
</term> by frequency of <term> events </term> . With relevant approach , we identify important
language </term> based on <term> logic </term> . With the aid of a <term> logic-based grammar formalism
generation </term> emerge from this method . With a parsimonious <term> instantiation scheme
speakers </term> prior to <term> training </term> . With only 12 <term> training speakers </term> for
</term> ( that of <term> Collins [ 1999 ] </term> ) with evidence from an additional 500,000 <term>
classification ( maximum entropy ) </term> with <term> linguistic information </term> . Instead
improvement in <term> recognition accuracy </term> with the <term> mixture trigram models </term> as
progress in the field , which we address with this work . Experiments with the <term> TREC
</term> results in 87.5 % <term> agreement </term> with a state of the art , proprietary <term> Arabic
. The results of this experiment , along with a preliminary analysis of the factors involved
language definition </term> are presented , along with a <term> control structure </term> for an <term>
class <term> NP </term> are reported , along with an <term> exponential time lower-bound </term>
tracking algorithm </term> is presented along with a description of its <term> implementation
</term> . Thus , our method can be applied with great benefit to <term> language pairs </term>
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