N13-1036 trans lation is also added to word-based translation . Results show that selecting
N03-1017 toolkit was developed to train word-based translation models from parallel corpora
P05-1068 this case , we back it off into a word-based translation model . In the word-based translation
P03-2040 phrase log likelihood ratio and word-based translation prob - ability . Un-shaded items
N04-4015 two-step morphological processing for word-based translation models . We first apply word
D12-1123 indicates that our method based on word-based translation model is effective for opinion
N13-1036 used to form an MSA lat - tice . Word-based translation . This category has two types
P05-1068 word-based translation model . In the word-based translation model , the constraints on the
E14-4018 quality with respect to the previous word-based translation models ( Brown et al. , 1990
N07-2009 translation . We have focused so far on word-based translation . In future work , we intend
N07-2035 ) has evolved from the initial word-based translation models to more advanced models
D12-1123 extract opinion targets by using word-based translation model ( WTM ) . We formulate
P05-1068 performance . Furthermore , since a word-based translation approach is often incapable of
N07-1062 language model , phrase-based and word-based translation models , word , phrase and distortion
J06-4004 sentence level were approximated from word-based translation models that were trained by using
D12-1123 Opinion Target Extraction Using Word-Based Translation Model 3.1 Method Framework As
D13-1175 fea - tures . Methods to learn word-based translation correspondences from supervised
N13-1036 we only use ` F2F + L2L ; S2S ' word-based translation mode . Phrase-based translation
D12-1123 Opinion Target Extraction Using Word-Based Translation Model </title> Kang Liu Liheng
D12-1123 " lcd screen " . 4.3 Effect of Word-based Translation Model In this subsection , we
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