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.
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. 12. A method as claimed in claim 1 wherein the questions are documents to be classified into specified categories and wherein the answers are the specified categories; and wherein the method comprises using the enhanced answers and the learnt expertise of the judges to update a knowledge base storing associations between the categories and references to the documents.