On Fri, Nov 20, 2009 at 10:08 AM, Grant Ingersoll <gsing...@apache.org>wrote:
> I should add in my $0.02 on whether to just get rid of queryNorm() > altogether: > > -1 from me, even though it's confusing, because having that call there > (somewhere, at least) allows you to actually do compare scores across > queries if you do the extra work of properly normalizing the documents as > well (at index time). > > > Do you have some references on this? I'm interested in reading more on the > subject. I've never quite been sold on how it is meaningful to compare > scores and would like to read more opinions. > References on how people do this *with Lucene*, or just how this is done in general? There are lots of papers on fancy things which can be done, but I'm not sure where to point you to start out. The technique I'm referring to is really just the simplest possible thing beyond setting your weights "by hand": let's assume you have a boolean OR query, Q, built up out of sub-queries q_i (hitting, for starters, different fields, although you can overlap as well with some more work), each with a set of weights (boosts) b_i, then if you have a training corpus (good matches, bad matches, or ranked lists of matches in order of relevance for the queries at hand), *and* scores (at the q_i level) which are comparable, then you can do a simple regression (linear or logistic, depending on whether you map your final scores to a logit or not) on the w_i to fit for the best boosts to use. What is critical here is that scores from different queries are comparable. If they're not, then queries where the best document for a query scores 2.0 overly affect the training in comparison to the queries where the best possible score is 0.5 (actually, wait, it's the reverse: you're training to increase scores of matching documents, so the system tries to make that 0.5 scoring document score much higher by raising boosts higher and higher, while the good matches already scoring 2.0 don't need any more boosting, if that makes sense). There are of course far more complex "state of the art" training techniques, but probably someone like Ted would be able to give a better list of references on where is easiest to read those from. But I can try to dredge up some places where I've read about doing this, and post again later if I can find any. -jake
