Technique based on Relevance Feeback & other Parameters
: package. By implementing new type of tuple (Query,Weight,Scorer) I can
: easily implement new Scoring technique. Unfortunatly Lucene index shows
that
: it stores only TF / Position vectors for each term within document.
: I
Technique based on Relevance Feeback & other Parameters
: package. By implementing new type of tuple (Query,Weight,Scorer) I can
: easily implement new Scoring technique. Unfortunatly Lucene index shows
that
: it stores only TF / Position vectors for each term within document.
: I
: package. By implementing new type of tuple (Query,Weight,Scorer) I can
: easily implement new Scoring technique. Unfortunatly Lucene index shows that
: it stores only TF / Position vectors for each term within document.
: I am interested in investigating new scoring technique where I w
Indeed - you bring up interesting questions. You may want to take a look at
NUTCH first, however - I am not sure if they have done some of the
Google-like ranking you mention.
However - collaborative relevance enhancement, based on user feedback, would
be a nice Web-2.0-ish feature to bake into th
I have a similar interest in specifying a custom scoring strategy. I
previously posted about it under the subject "Scoring a document
(count?)" on 7/27/06. In brief, I want a documents score to be a count
of term matches. This is nearly identical to a SQL count()
functionality.
If you are able
On Aug 23, 2006, at 8:30 AM, sachin wrote:
Hello Great/smart guys
This is my first question for this group as I started
working on the Lucene last month.
Lucene provide the scoring of documents based on TF-IDF
vector analysis. Lucene also provides the Scorer and Weight i
