Hi List,
I am pretty new to Lucene. Certainly, it is very exciting. I need to
implement a new Similarity class based on the Term Vector Space Model given
in http://www.miislita.com/termvector/termvector3.html
Although that model is similar to Lucene’s model
(http://hudson.zones.apache.org/hudson/job/Lucenetrunk/javadoc//org/apache/lucene/search/Similarity.html),
I am having hard time to extend the Similarity class to calculate that
model.
In that model, “tf” is multiplied with Idf for all terms in the index, but
in Lucene “tf” is calculated only for terms in the given Query. Because of
that effect, the norm calculation should also include “idf” for all terms.
Lucene calculates the norm, during indexing, by “just” counting the number
of terms per document. In the web formula (in miislita.com), a document norm
is calculated after multiplying “tf” and “idf”.
FYI: I could implement “idf” according to miisliat.com formula, but not the
“tf” and “norm”
Could you please comment me how I can implement a new Similarity class that
will fit in the Lucene’s architecture, but still implement the vector space
model given in miislita.com
Thanks a lot for your comments,
Dharma

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