W04-3249 Gaussian Mixture algorithm for domain relevance estimation is m = 2 . Each component j is
W04-3249 evaluate the accuracy of the domain relevance estimation technique described above is
W06-3004 entire sequence . We have performed relevance estimation using Conditional Random Fields
W04-3249 Gaussian Mixture algorithm for domain relevance estimation exploits a Gaussian Mixture to
W04-3249 Texts The basic idea of domain relevance estimation for texts is to exploit lexical
W04-3249 this paper we introduce Domain Relevance Estimation ( DRE ) a fully unsupervised
W04-0856 unsupervised technique - Domain Relevance Estimation ( DRE ) - for domain detection
P07-1099 shows that the quality of answer relevance estimation is significantly affected by
W04-3249 explained in Section 1 Domain Relevance Estimation is not a common Text Categorization
W13-4053 a modification of fuzzy rules relevance estimation for fuzzy classifier . Using
W13-4053 present a new formula for term relevance estimation , which is a modification of
W04-3249 frequency count is then inadequate for relevance estimation : irrelevant senses of ambiguous
W08-1406 relevant to pursue new methods for relevance estimation . On the other hand , automatically
W04-3249 . In both cases the new domain relevance estimation technique improves the performance
W04-3249 Formula ( 2 ) defines the domain relevance estimation function ( re - member that d
W04-0856 Senseval-2 DDD system with the Domain Relevance Estimation ( DRE ) tech - nique . Given
N07-2006 then sorted according to their relevance estimation and the top n phrase pairs were
W04-0856 which term similarity and domain relevance estimation is required . 3 All-Words systems
W04-3249 Abstract This paper presents Domain Relevance Estimation ( DRE ) , a fully unsupervised
W06-3004 models provided in Figure 4 . 4.2 Relevance Estimation using Conditional Random Fields
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