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I am new to IR techniques.
I looking for a Java based API or tool that does the following.
Kindly let me know how can Lucene be helpful to me.
Regards Yuvi
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idf(t,D) = log (N/( n))N is the number of documents in the data set. n is the number of documents that contain the term t among the data set. Finally TFIDF is calculated as the product of the above two values.
The TF-IDF of a term is calculated by multiplying TF and IDF scores. Translated into plain English, importance of a term is high when it occurs a lot in a given document and rarely in others. In short, commonality within a document measured by TF is balanced by rarity between documents measured by IDF.
The formula that is used to compute the tf-idf for a term t of a document d in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is computed as idf(t) = log [ n / df(t) ] + 1 (if smooth_idf=False ), where n is the total number of documents in the document set and df(t) is the document frequency of t; the ...
TF-IDF is used by search engines to better understand the content that is undervalued. For example, when you search for “Coke” on Google, Google may use TF-IDF to figure out if a page titled “COKE” is about: a) Coca-Cola. b) Cocaine.
You could try the Word Vector Tool - it's been a while since the latest release, but it works fine here. It should be able to perform all of the steps you mention. I've never used the crawler part myself, however.
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Actually, TF-IDF is a score given to a term in a document, rather than the whole document. If you just want the TF-IDFs per term in document, maybe use this method, without ever touching Lucene. If you want to create a search engine, you need to do a bit more (such as extracting text from the given URLs, whose corresponding documents would probably not contain raw text). If this is the case, consider using Solr.
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