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/term-vector/term-vector-3.html
Although that model is similar to Lucene’s model (http://hudson.zones.apache.org/hudson/job/Lucene-trunk/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 -- View this message in context: http://www.nabble.com/Vector-Space-Model%3A-New-Similarity-Implementation-Issues-tp15696719p15696719.html Sent from the Lucene - Java Users mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
