| P04-1064 | therefore an orthogonal , non-negative | factorisation | of the original translation matrix |
| P04-1064 | Example We first illustrate the | factorisation | process on a simple example . |
| P09-1088 | This is achieved by using the | factorisation | algorithm of Zhang et al. ( 2008a |
| P04-1064 | However it is easy to show that the | factorisation | is optimally done on a sentence |
| P04-1064 | Table 4 shows that the matrix | factorisation | approach does not offer any quantitative |
| P04-1064 | sentences One natural comment on our | factorisation | scheme is that cepts should not |
| E09-3001 | word-like ' entities . However , the | factorisation | process removes all temporal |
| P13-1077 | alignment is used as input to the rule | factorisation | algorithm , producing the ITG |
| P04-1064 | teed , leading to a non-negative | factorisation | of M . The second step of our |
| D14-1111 | scheme and nonnegative matrix | factorisation | ( NMF ) ( Grefenstette et al. |
| P04-1064 | evaluation of the use of matrix | factorisation | for aligning words , we tested |
| P04-1064 | <title> Aligning words using matrix | factorisation | </title> Cyril Goutte Kenji Yamada |
| P13-1077 | alignments constructed using a SCFG | factorisation | method ( Blunsom et al. , 2009a |
| N07-2038 | 2.4 State Transition Model The | factorisation | of S into A and G can now be |
| P04-1064 | data : onal non-negative matrix | factorisation | ( ONMF ) using the AIC and BIC |
| P04-1064 | instance of non-negative matrix | factorisation | , aka NMF ( Lee and Seung , 1999 |
| P04-1064 | the way M is non-negative matrix | factorisation | ( ONMF ) using the AIC and BIC |
| P04-1064 | clearly there is no chance that our | factorisation | algorithm will recover the alignment |
| E14-1025 | also tried non-negative matrix | factorisation | ( NNMF ) ( Seung and Lee , 2001 |
| P04-1064 | and can - not be handled by the | factorisation | method . Note that this is the |
