| W04-3249 | Gaussian Mixture algorithm for | domain relevance estimation | is m = 2 . Each component j is |
| W04-3249 | To evaluate the accuracy of the | domain relevance estimation | technique described above is |
| W04-3249 | Gaussian Mixture algorithm for | domain relevance estimation | exploits a Gaussian Mixture to |
| W04-3249 | Estimation for Texts The basic idea of | domain relevance estimation | for texts is to exploit lexical |
| W04-3249 | ) . In this paper we introduce | Domain Relevance Estimation | ( DRE ) a fully unsupervised |
| W04-0856 | fully unsupervised technique - | Domain Relevance Estimation | ( DRE ) - for domain detection |
| W04-3249 | itself . As explained in Section 1 | Domain Relevance Estimation | is not a common Text Categorization |
| W04-3249 | is 40 % . In both cases the new | domain relevance estimation | technique improves the performance |
| W04-3249 | it . 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 |
| W04-0856 | applications in which term similarity and | domain relevance estimation | is required . 3 All-Words systems |
| W04-3249 | Carlo Abstract This paper presents | Domain Relevance Estimation | ( DRE ) , a fully unsupervised |
| W04-3249 | placeholder " for all other domains . 3 | Domain Relevance Estimation | for Texts The basic idea of domain |
| W04-3249 | 6 Conclusions and Future Works | Domain Relevance Estimation | , an unsupervised TC technique |
| W04-0856 | both exploiting a new technique ( | Domain Relevance Estimation | ) for domain detection in texts |
