will demonstrate an application of this approach called <term> LCS-Marine </term> . Using <term>
confidence </term> . We describe a three-tiered approach for <term> evaluation </term> of <term> spoken
performance </term> . We describe our use of this approach in numerous fielded <term> user studies </term>
military . This paper proposes a practical approach employing <term> n-gram models </term> and <term>
of the same <term> source text </term> . Our approach yields <term> phrasal and single word lexical
operational semantics </term> . The value of this approach is that as the <term> operational semantics
tech,32-1-P01-1056,bq hand-crafted template-based or rule-based approaches </term> . In this paper We experimentally
tech,21-1-N03-1004,bq developed a <term> multi-strategy and multi-source approach to question answering </term> which is based
tech,2-1-N03-2025,bq quality </term> . A novel <term> bootstrapping approach </term> to <term> Named Entity ( NE ) tagging
successive learners </term> is presented . This approach only requires a few <term> common noun </term>
</term> . The resulting <term> NE system </term> approaches <term> supervised NE </term> performance for
tech,0-4-N03-3010,bq robustness and flexibility . <term> Statistical approach </term> is much more robust but less accurate
<term> corpus data </term> . We describe a new approach which involves clustering <term> subcategorization
tech,3-1-P03-1022,bq </term> . We apply a <term> decision tree based approach </term> to <term> pronoun resolution </term>
tech,4-1-P03-1050,bq paper presents an <term> unsupervised learning approach </term> to building a <term> non-English (
be given for <term> Arabic </term> , but the approach is applicable to any <term> language </term>
tech,1-6-P03-1050,bq removal </term> . Our <term> resource-frugal approach </term> results in 87.5 % <term> agreement </term>
learning </term> . In this paper , we evaluate an approach to automatically acquire <term> sense-tagged
accuracy </term> difference between the two approaches is only 14.0 % , and the difference could
presents a <term> machine learning </term> approach to bare <term> sluice disambiguation </term>
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