21. The audiovisual-file-tagging system according to claim 15 , further comprising: a revised estimate presenting unit configured to present to the user, for user input selected in the group consisting of ratification and correction, the revised estimate of the values of the respective attributes of the audiovisual work represented by said audiovisual file; wherein, if the user input consists of correction of the revised estimate, the revised estimate presenting unit is further configured to use the corrected estimate to calculate a further-revised estimate of the values of the respective attributes of the audiovisual work represented by said audiovisual file.
15. An audiovisual-file-tagging system configured to produce a set of tags for an input audiovisual file, the set of tags indicating values of attributes of an audiovisual work of defined type represented by said audiovisual file, the system comprising: a prompt-issuing unit configured to issue, to a user, a prompt for manual assignment of tags to said input audiovisual file; an initial estimate unit configured to input, as an initial estimate of the values of the respective attributes of the audiovisual work represented by said audiovisual file, data representative of the tags assigned to said input audiovisual file by the user in response to said prompt; a correlation function application unit having a processor configured to automatically apply a set of one or more correlation functions to the attribute-value estimates of said initial estimate, to produce a set of revised estimates, said processor being further configured to assign a respective confidence measure to each attribute value of said revised estimates; and a final result outputting unit configured to output the final result of the applying step as the set of tags for said input audiovisual file; wherein the correlation functions applied by said correlation function application unit are functions embodying the correlations holding between known attribute-values of a set of training examples, said training examples being audiovisual works of said defined type corresponding to manually-tagged audiovisual files, and wherein said processor is further configured to apply the correlation function application step iteratively, to apply said correlation functions only to attribute-values estimates associated with a confidence measure whose value exceeds a threshold, and to set said confidence measure to a maximum value for attribute values provided by the user as the initial estimate to ensure that said attribute values provided by the user as the initial estimate are not changed.