A primitive attempt here: Tagger V2. Two unique features, half-useful, but still interesting (I think).
I’m neither a programmer nor have knowledge in AI or ML. I am just trying to use the “see-also” to do the heavy lifting work. I think whether auto tagging can be successfully done in DT is depending on (1) the objective/nature of tagging, (2) the word count of the items within the database, (3) consistency of existing groups, (4) or as simple as naming of the file.
A very systemic naming system can achieve high quality auto-tagging simply by word match.
See-also tends to give relatively good quality of common-tags-based suggestion.
A database with very consistent and distinctive groups in the database may/will help to narrow down a more targeted set of common tags (only extract the common tags from those see-also documents that are within the higher scored classify-to group).
Unless frequency count is the main criteria of tagging, I think concordance is too “raw” to be used for perception- or interpretation-based tagging because concordance in itself doesn’t constructed schemata. See-also and classify are the processed products of concordance so they may be more useful.
Just a very non-professional reading of auto-tagging, so u need to forgive my naive comment!