M98-1014 |
conclusion about the sem value ,
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evidence combination
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comes into play . We combine
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W04-1603 |
studying the feature matrix the
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evidence combination
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strategies for stage four can
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P11-1116 |
factor . Phase II : The nonlinear
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evidence combination
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formulas in the previous subsection
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W04-1603 |
research we hope to extend the
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evidence combination
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algorithms to include more sophisticated
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D14-1087 |
the viewpoint of probabilistic
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evidence combination
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. We first demonstrate that the
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P11-1116 |
the model , we design a set of
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evidence combination
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and propagation algo - rithms
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W04-1603 |
denominator . The information used for
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evidence combination
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was as follows . Evidence used
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W04-1806 |
this paper . 5 Conclusion The
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evidence combination
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described above is based on transitivity
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P11-1116 |
w.r.t. a term ) with probabilistic
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evidence combination
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. The model was studied in (
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W04-0821 |
test data , and also to explore
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evidence combination
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from multiple language pairs
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N07-3009 |
evidence combination ? By exploring
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evidence combination
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, this dissertation will advance
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N07-3009 |
source , e.g. , ( Ng , 2000 ) . Can
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evidence combination
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decrease domain de - pendency
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W04-1806 |
kind-of ` biological process ' . The
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evidence combination
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, in addition to computing transitive
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C90-2071 |
integration have used nonprobabilistic
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evidence combination
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schemes . Schubert ( 1986 ) suggests
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W04-1806 |
experimenting with an ad hoc weighted
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evidence combination
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scheme , based on each knowledge
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N07-3009 |
several ways . Can we improve SDR by
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evidence combination
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? By exploring evidence combination
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H93-1031 |
the Dempster-Shafer Theory of
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Evidence Combination
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\ -LSB- Shafer , 1976 \ -RSB-
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J88-3002 |
triggering of another stereotype ,
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evidence combination
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rules are invoked to resolve
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J07-1005 |
statistical methods ( classification ,
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evidence combination
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, etc. ) . We have taken an important
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N09-1021 |
unlikely to be helpful for downstream
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evidence combination
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( where we may prefer to keep
|