| Cross-Lingual Mixture Model for Sentiment Classification | The parameters to be estimated include conditional probabilities of word to class, P(w8|c) and P(wt|c), and word projection |
| Cross-Lingual Mixture Model for Sentiment Classification | The obtained word-class conditional probability P (wt |c) can then be used to classify text in the target languages using Bayes Theorem and the Naive Bayes |
| Cross-Lingual Mixture Model for Sentiment Classification | Instead of estimating word projection probability (P(w3|wt) and P(wt|w3)) and conditional probability of word to class (P(wt|c) and P(w3|c)) simultaneously in the training procedure, we estimate them separately since the word projection probability stays invariant when estimating other parameters. |
| Alignment Methods | These models are by nature directional, attempting to find the alignments that maximize the conditional probability of the target sentence P(e{| f1], aK For computational reasons, the IBM models are restricted to aligning each word on the target side to a single word on the source side. |
| Substring Prior Probabilities | In addition, we heuristically prune values for which the conditional probabilities P(e| f) or P(f|e) are less than some fixed value, which we set to 0.1 for the reported experiments. |
| Substring Prior Probabilities | To determine how to combine 0(6), 0( f ), and C(6, f) into prior probabilities, we performed preliminary experiments testing methods proposed by previous research including plain co-occurrence counts, the Dice coefficient, and X—squared statistics (Cromieres, 2006), as well as a new method of defining substring pair probabilities to be proportional to bidirectional conditional probabilities |
| Substring Prior Probabilities | Z = Z Pcooc(e|f)Pcooc(f|e)' {6,f;c(e,f)>d} The experiments showed that the bidirectional conditional probability method gave significantly better results than all other methods, so we adopt this for the remainder of our experiments. |