| Discriminative Synchronous Transduction | Our log-linear translation model defines a conditional probability distribution over the target translations of a given source sentence. |
| Discriminative Synchronous Transduction | The conditional probability of a derivation, d, for a target translation, e, conditioned on the source, f, is given by: |
| Discriminative Synchronous Transduction | Given (1), the conditional probability of a target translation given the source is the sum over all of its derivations: |
| Evaluation | This is illustrated in Table 2, which shows the conditional probabilities for rules, obtained by 10-cally normalising the rule feature weights for a simple grammar extracted from the ambiguous pair of sentences presented in DeNero et al. |
| Evaluation | The first column of conditional probabilities corresponds to a maximum likelihood estimate, i.e., without regularisation. |
| Methods | Being a stopword in our case, and having no relevance at all to speculative assertions, it has a class conditional probability of P(spec|it) = 74.67% on the seed sets. |
| Methods | We ranked the features c by frequency and their class conditional probability P(spec|cc). |
| Methods | Maximum Entropy Models (Berger et al., 1996) seek to maximise the conditional probability of classes, given certain observations (features). |