Thanx On Fri, Jul 9, 2010 at 1:10 PM, Uwe Schindler <u...@thetaphi.de> wrote:
> > Thanks for your valuble comments. Yes I observed tha, once the number of > > terms of the goes up, fieldNorm value goes down correspondingly. I think, > > therefore there won't be any default due to the variation of total number > of > > terms in the document. Am I right? > > With the current scoring model advanced statistics are not available. There > are currently some approaches to add BM25 support to Lucene, for what the > index format needs to be enhanced to contain more statistics (number of > terms per document, avg number of terms per document,...). > > > On Thu, Jul 8, 2010 at 9:34 AM, Rebecca Watson <bec.wat...@gmail.com> > > wrote: > > > > > hi, > > > > > > > 1) Although Lucene uses tf to calculate scoring it seems to me that > > > > term frequency has not been normalized. Even if I index several > > > > documents, it does not normalize tf value. Therefore, since the > > > > total number of words in index documents are varied, can't there be > > > > a fault in Lucene's > > > scoring? > > > > > > tf = term frequency i.e. the number of times the term appears in the > > > document, while idf is inverse document frequency - is a measure of > > > how rare a term is, i.e. related to how many documents the term > > > appears in. > > > > > > if term1 occurs more frequently in a document i.e. tf is higher, you > > > want to weight the document higher when you search for term1 > > > > > > but if term1 is a very frequent term, ie. in lots of documents, then > > > its probably not as important to an overall search (where we have > > > term1, term2 etc) so you want to downweight it (idf comes in) > > > > > > then the normalisations like length normalisation (allow for 'fair' > > > scoring across varied field length) come in too. > > > > > > the tf-idf scoring formula used by lucene is a scoring method that's > > > been around a long long time... there are competing scoring metrics > > > but that's an IR thing and not an argument you want to start on the > > > lucene lists! :) > > > > > > these are IR ('information retrieval') concepts and you might want to > > > start by going to through the tf-idf scoring / some explanations for > > > this kind of scoring. > > > > > > http://en.wikipedia.org/wiki/Tf%E2%80%93idf > > > http://wiki.apache.org/lucene-java/InformationRetrieval > > > > > > > > > > 2) What is the formula to calculate this fieldNorm value? > > > > > > in terms of how lucene implements its tf-idf scoring - you can see > here: > > > http://lucene.apache.org/java/3_0_2/scoring.html > > > > > > also, the lucene in action book is a really good book if you are > > > starting out with lucene (and will save you a lot of grief with > > > understanding lucene / setting up your application!), it covers all > > > the basics and then moves on to more advanced stuff and has lots of > > > code examples too: > > > http://www.manning.com/hatcher2/ > > > > > > hope that helps, > > > > > > bec :) > > > > > > --------------------------------------------------------------------- > > > To unsubscribe, e-mail: java-user-unsubscr...@lucene.apache.org > > > For additional commands, e-mail: java-user-h...@lucene.apache.org > > > > > > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: java-user-unsubscr...@lucene.apache.org > For additional commands, e-mail: java-user-h...@lucene.apache.org > >