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.
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. 22. The audiovisual-file tag production method according to claim 15 , wherein the correlation-function application unit is further configured, after each iteration, to determine the best estimate so far of the value of each attribute, wherein the first iteration applies correlation functions to said initial estimate of attribute-values, and subsequent iterations apply correlation functions to the best estimates so far of said attribute-values.