int e = entry.getRightElement();
String eTerm = eVocab_f2e.get(e);
// LOG.info("Pr("+eTerm+"|"+token+")="+probEF);
if (probEF > 0 && e > 0 && !docLangTokenizer.isStopWord(eTerm) && (translateOnly == null || !translateOnly.equals("indri") || indriPuncPattern.matcher(eTerm).matches()) && (pairsInSCFG == null || pairsInSCFG.contains(new PairOfStrings(token,eTerm)))) {
// assuming our bilingual dictionary is learned from normally segmented text, but we want to use bigram tokenizer for CLIR purposes
// then we need to convert the translations of each source token into a sequence of bigrams
// we can distribute the translation probability equally to the each bigram
if (bigramSegment) {
String[] eTokens = docLangTokenizer.processContent(eTerm);