| J14-2003 | advantage of not requiring any | topic inference | in the classification phase . |
| D09-1092 | documents , most likely due to better | topic inference | . Results vary by language . |
| P14-2036 | consists of three steps : ( a ) | Topic inference | for external knowledge by running |
| P11-3006 | 2010 ) proposed a flexible latent | topics inference | in which topics are assigned |
| E14-1027 | task from the classical task of | topic inference | . Figure 1 presents a plate diagram |
| E12-1037 | multinomial mixture model , for | topic inference | inside threads . We build a system |
| N10-1074 | add very much , at least for the | topic inference | task . To evaluate how well we |
| E14-1035 | translation probabilities After | topic inference | on the tuning and test data , |
| P11-2118 | which the - otherwise unaltered - | topic inference | algorithm is to be applied . |
| P14-2036 | running LDA estimation . ( b ) | Topic inference | for microblogs by employing the |
| E14-1035 | each phrase pair . 3 Bilingual | topic inference | 3.1 Inference on training documents |
| P14-2036 | including an approach for short text | topic inference | and adds abstract words as extra |
| P14-2036 | contributions are : ( 1 ) We formulate the | topic inference | problem for short texts as a |
| P06-1003 | HMM ) to model segmentation and | topic inference | for text using a bigram representation |
| I05-5009 | Table 10 , we can conclude that | topic inference | by latent variable models resembles |
| D14-1138 | remains an open problem in spectral | topic inference | . We have shown that previous |
| I05-5009 | . 2 Latent Variable Models and | Topic Inference | In this section , we introduce |
| P14-2036 | step ( a ) , the method used for | topic inference | for microblogs is not directly |
| D12-1059 | sampling algorithms for online | topic inference | : ( i ) o - LDA , ( ii ) incremental |
| P14-2036 | obtained from step ( a ) . 3.1 | Topic Inference | for External Knowledge We do |
