some results about the effectiveness of these <term> indices </term> can be obtained . To
translation ( MT ) systems </term> . We believe that these <term> evaluation techniques </term> will provide
( as ) </term> , and <term> besides </term> . These <term> words </term> appear frequently enough
models </term> of <term> WH-questions </term> . These <term> models </term> , which are built from
<term> unknown word features </term> . Using these ideas together , the resulting <term> tagger
on both <term> systems </term> . Motivated by these arguments , we introduce a number of new
<term> negative feedback </term> . Based on these results , we present an <term> ECA </term>
clauses </term> can be learnt automatically from these <term> features </term> . We suggest a new
eventual objective of this project is to use these <term> summaries </term> to assist <term> help-desk
A <term> support vector machine </term> uses these <term> features </term> to capture <term> breakdowns
documents </term> . Despite the successes of these systems , <term> accuracy </term> will always
literature on <term> machine translation </term> . These <term> models </term> can be viewed as pairs
define an initial <term> ranking </term> of these <term> parses </term> . A second <term> model
features </term> , without concerns about how these <term> features </term> interact or overlap
<term> generative model </term> which takes these <term> features </term> into account . We introduce
efficient <term> decoder </term> and show that using these <term> tree-based models </term> in combination
polynomial time solution </term> for any of these <term> hard problems </term> ( unless <term>
polynomial time approximations </term> for these computations . We also discuss some practical
is sufficiently general to be applied to these diverse problems , discuss its application
commas </term> . Finally , we have shown that these results can be improved using a bigger
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