1. A method of obtaining enhanced answers to a plurality of questions comprising: accessing features comprising any one or more of: at least one feature for each of a plurality of judges, and at least one feature for each of the questions; accessing at least one answer for each question given by one of the judges and, for a plurality of the questions, more than one answer given by different ones of the judges; using a probabilistic learning system to learn an expertise of each judge using the features, the probabilistic learning system using at least a first question for which an answer is known as ground truth and a second question for which a ground truth answer is unknown, true answers to at least some of the questions being unknown to the probabilistic learning system and true expertise of the judges for the questions being unknown to the probabilistic learning system, an enhanced answer to a question being more likely to be accurate than a corresponding answer before application of the probabilistic learning system; and using the probabilistic learning system to determine enhanced answers to the questions by aggregating the answers in a manner which takes into account the identified expertise of the judges by weighting answers of the judges based on the identified expertise of the judges.
4. A method as claimed in claim 1 wherein using the probabilistic learning system to determine enhanced answers to the questions comprises using an assessment component of the probabilistic learning system having a graphical structure for aggregating the answers comprising, a node representing a learnt enhanced answer connected to, one node for each observed answer to be aggregated to the learnt enhanced answer, each of the nodes for the observed answers being connected to at least one node representing a learnt expertise indicator of a judge who gave the observed answer.