On 27/03/2012 20:25, Zeynep P. wrote:
> While using the pruning package, I realised that ridf is calculated in
> RIDFTermPruningPolicy as follows:
> Math.log(1 - Math.pow(Math.E, termPositions.freq() / maxDoc)) - df
>
> However, according to the original paper (Blanco et al.) for residual idf,
> it should be -log(df/D) + log (1 - e^(*-*tf/D)). Thus, in the equation,
> Math.pow should be Math.pow(Math.E, - (termPositions.freq() / maxDoc))
>
> Do I miss something in the calculation or is this a bug?
Hmm, good question! After checking the original paper again, and then
checking our implementation, I think that this is indeed a bug, and we
should add the minus there, but ... this formula may be completely
broken either way. The paper that you mention
(http://www.dc.fi.udc.es/~barreiro/publications/blanco_barreiro_ecir2007.pdf)
says thus:
"Residual idf is defined in [3] as the difference between the observed
idf (IDF ) and the idf expected under the assumption that the terms
follow an independence model, such as Poisson (IDF^). [...] If tf is the
total number of tokens for a term t, then the ridf devised by a Poisson
distribution is
RIDF = IDF − IDF^ = −log(df/D) + log(1 − e^(-tf/D)) [2]
"
Since the purpose of the RIDF metric is to select informative words
collection-wide, and not per-document, then it makes sense that they use
a collection-wide metric like IDF as a baseline vs. another
collection-wide metric based on total term frequency, or rather the
total number of term occurrences in a collection.
The problem in our implementation is that we use a within-document term
frequency (the number of occurrences of t in the current document) and
not a collection-wide term frequency... so, it looks to me that the fix
would be to first fully traverse the doc enumeration and calculate the
total number of term occurrences in all documents (e.g. in
RIDFTermPruningPolicy.initPositionsTerm(..) ), and use this value in the
formula in place of termPositions.freq().
--
Best regards,
Andrzej Bialecki <><
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