participants are using . In this presentation , we describe the features of and <term> requirements </term>
a standard <term> text browser </term> . We describe how this information is used in a <term>
with the best <term> confidence </term> . We describe a three-tiered approach for <term> evaluation
and <term> component performance </term> . We describe our use of this approach in numerous fielded
the <term> hand-crafted system </term> . We describe a set of <term> supervised machine learning
among the target variables . This paper describes a method for <term> utterance classification
character recognition ( OCR ) model </term> that describes an end-to-end process in the <term> noisy
using the <term> language model </term> . We describe a simple <term> unsupervised technique </term>
<term> NE types </term> . In this paper , we describe a <term> phrase-based unigram model </term>
undisambiguated <term> corpus data </term> . We describe a new approach which involves clustering
evaluating <term> WSD programs </term> . We describe the ongoing construction of a large , <term>
WSD system </term> , implementing the method described herein showed very encouraging results
<term> translation performance </term> . We describe a hierarchy of <term> loss functions </term>
e.g. , <term> thesis statements </term> ) . We describe a new system that enhances <term> Criterion
application of existing <term> metrics </term> . We describe a <term> method </term> for identifying systematic
approach </term> to <term> ranking problems </term> described in <term> Freund et al. ( 1998 ) </term> .
resources </term> are available . In this paper we describe a novel <term> data structure </term> for <term>
loss in <term> translation quality </term> . We describe a novel <term> approach </term> to <term> statistical
<term> tree-based ordering model </term> . We describe an efficient <term> decoder </term> and show
<term> parallel corpora </term> . Second , we describe the <term> graphical model </term> for the <term>
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