Hi,
can you provide a minimal example (no. of sentences max 5)? 1 -> 0.85
seems a rather big decrease in score to me, so unless you removed the
longest sentence with the rarest words in the collection, I smell some
bug, e.g. you forgot to remove it from the denominator as well, etc.
It would also be a good idea to compute the distance without IDF
weighting to see if you experience a similar effect.
Regards,
David Nemeskey
Quoting Kasun Perera <kas...@opensource.lk>:
Hi all
I’m indexing collection of documents using Lucene specifying TermVerctor at
the indexing time. Then I retrieve terms and their term frequencies by
reading the index and calculate TF-IDF scores vector for each document.
Then using TF-IDF vectors, I calculate pairwise cosine similarity between
documents using the equation here
http://en.wikipedia.org/wiki/Cosine_similarity.
This is my problem
Say I have two identical documents “A” and “B” in this collection (A and B
have more than 200 sentences).
If I calculate pairwise cosine similarity between A and B it gives me
cosine value=1 which is perfectly OK.
But If I remove a single sentence from Doc “B”, it gives me cosine
similarity value around 0.85 between these two documents. The documents are
almost similar but cosine values are not. I understand the problem is with
the equation that I’m using.
Is there better way/ better equation that I can use for calculating cosine
similarity between documents?
--
Regards
Kasun Perera
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