N03-2016 achieved without modifying the statistical training algorithm . 2 The method We experimented
A92-1025 knowledge-based methods with statis - tics , statistical training replaces knowledge engineering
H94-1028 automatically through another statistical training procedure \ -LSB- 3 \ -RSB- .
S15-1029 gains brought by the corpus and a statistical training . We split the generated corpus
E93-1040 trivial and therefore requires statistical training . In the appendix we give examples
A00-1034 variable and thus lend themselves to statistical training algorithms such as HMMs . Finally
A92-1025 NLP by proving the utility of statistical training methods on a knowledge-based
C00-2159 approaches use parallel corpora as statistical training data and then retrieve documents
P01-1027 computed automatically using another statistical training procedure ( Och , 1999 ) which
P98-2167 is a hybrid system using both statistical training and rule-based training . Rule-based
W01-1409 translation model and the algorithms for statistical training be improved so that they require
N03-4015 technique we investigated used a statistical training method to build a model to translate
H92-1041 built by human engi - neering , statistical training , or a combination of the two
W04-2313 words could serve as features for statistical training . More data and more reliable
W05-1206 develop adequate features for statistical training . It might also be thought that
H89-1042 pronunciations . For systems which use statistical training of models of speech segments
W04-2313 are significantly improved . 8 Statistical Training of DM Classifiers The relevance
N09-2055 RBMT system . On the other hand , statistical training and translation in both SMT and
A92-1025 Acquisition The strategies for statistical training described here all use a " training
P96-1020 uniformly to user-defined patterns . Statistical training of patterns can also be incorporated
hide detail