Hi all,
Lucene score document based on the correlation between
the query q and document t:
(this is raw function, I don't pay attention to the
boost_t, coord_q_d factor)
score_d = sum_t( tf_q * idf_t / norm_q * tf_d * idf_t
/ norm_d_t) (*)
Could anybody explain it in detail ? Or are there any
papers, documents about this function ? Because:
I have also read the book: Modern Information
Retrieval, author: Ricardo Baeza-Yates and Berthier
Ribeiro-Neto, Addison Wesley (Hope you have read it
too). In page 27, they also suggest a scoring funtion
for vector model based on the correlation between
query q and document d as follow (I use different
symbol):
sum_t( weight_t_d * weight_t_q)
score_d(d, q)= --------------------------------- (**)
norm_d * norm_q
where weight_t_d = tf_d * idf_t
weight_t_q = tf_q * idf_t
norm_d = sqrt( sum_t( (tf_d * idf_t)^2 ) )
norm_q = sqrt( sum_t( (tf_q * idf_t)^2 ) )
(**): sum_t( tf_q*idf_t * tf_d*idf_t)
score_d(d, q)=--------------------------------- (***)
norm_d * norm_q
The two function, (*) and (***), have 2 differences:
1. in (***), the sum_t is just for the numerator but
in the (*), the sum_t is for everything. So, with
norm_q = sqrt(sum_t((tf_q*idf_t)^2)); sum_t is
calculated twice. Is this right? please explain.
2. No factor that define norms of the document: norm_d
in the function (*). Can you explain this. what is the
role of factor norm_d_t ?
One more question: could anybody give me documents,
papers that explain this function in detail. so when I
apply Lucene for my system, I can adapt the document,
and the field so that I still receive the correct
scoring information from Lucene .
Best regard,
Thanks every body,
=====
Ð#7863;ng Nhân
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