Hi All,
Greetings,
Just started with Lucene 5.1 a month ago for my research. I have a set
of documents indexed with term frequencies option enabled during indexing.
For given any two documents, I would like to calculate their tfidf cosine
similarity could you please point me to the right
It's not hard to implement one. Store your term value of your document with
payload. Then create your own Query and override the score function with
your cosine similarity logic.
The problem here is you need to watch out the performance, especially for
terms have very high DF. It may dec
Hi,
I would like to calculate raw cosine similarity between query and
document. I read documentation about lucene scoring but I'm still
confused. Does exist any implementation in Luscen 4.3.0 to do that. If
not, what is the easiest way to do this.
So far I'm retrieving a TermVector fo
Dear Users,
I'm calculation cosine similarity between two documents using code based
on the code at this link...
http://sujitpal.blogspot.ch/2011/10/computing-document-similarity-using.html
Is it working fine, but I want to use terms from two different fields in
my indexed docu
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 identi
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
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
calculate the TFIDF values for
documents then calculate the cosine similarity using TFIDF.
The field.setboost() function will give NO effect on term Frequencies.
Is there anyother way to do the boosting that will give effect
on term-frequencies?
Thanks
>
> Best
> Erick
>
> On F
ew Field(docNames[curDocNo], strRdElt,
> Field.TermVector.YES);*
>
>
>
> I’m using Lucene index .TermFreqVector functions to calculate TFIDF values
> and, then calculate cosine similarity between two documents using TFIDF
> values.
>
>
> For give weights to Ontology an
Field.Index.ANALYZED, Field.TermVector.YES);*
*Field document = new Field(docNames[curDocNo], strRdElt,
Field.TermVector.YES);*
I’m using Lucene index .TermFreqVector functions to calculate TFIDF values
and, then calculate cosine similarity between two documents using TFIDF
values.
For give weights to O
Update:
I actually don't understand why if the scores are substantially the cosine
similarity between query and the docs, such scores are not comparable
between queries.
Isn't cosine similarity describing the divergence between vectors ? If I
have vector A and B (my queries) and vecto
need to find a comparable score across queries, and more
specifically the cosine similarity... as similarity measure between my query
document and the documents in the collection.
could you give me some tip about it ?
thanks
There is a MoreLikeThis similarity search class in Lucene, it should
do what you're looking for.
http://lucene.apache.org/java/2_4_1/api/org/apache/lucene/search/similar/MoreLikeThis.html
Cheers,
Anthony
On Fri, Sep 11, 2009 at 11:25 PM, Alexy Khrabrov wrote:
> Given that I have a field for whi
Given that I have a field for which term vector was computed and stored, and
that field is the text of a document, I'd like to rank a subset of such
documents by similarity to a given held-out document, or query, directly
using the cosine measure. How can that be done without going through
creatin
I would like to know if there is a simple way to force Lucene to adopt the
simple cosine similarity of the term frequency vectors of the documents and
the query for ranking the result. In practice the score sc_i of the document
i should be given by:
sc_i = (D_i*Q)/(|D_i|*|Q|)
where D_i = vector
I would like to know if there is a simple way to force Lucene to adopt the
simple cosine similarity of the term frequency vectors of the documents and
the query for ranking the result.
Thank you
Claudio
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To unsubscribe, e
p, HBase, UIMA, NLP, NER, IR
>
>
>
> - Original Message
>> From: starz10de
>> To: java-user@lucene.apache.org
>> Sent: Friday, July 24, 2009 4:50:22 PM
>> Subject: Cosine similarity
>>
>>
>> Does lucene use cosine smiliarity measu
0de
> To: java-user@lucene.apache.org
> Sent: Friday, July 24, 2009 4:50:22 PM
> Subject: Cosine similarity
>
>
> Does lucene use cosine smiliarity measure to measure the similarity between
> the query and the indexed documents?
>
> Thanks
> --
> View this message
Does lucene use cosine smiliarity measure to measure the similarity between
the query and the indexed documents?
Thanks
--
View this message in context:
http://www.nabble.com/Cosine-similarity-tp24651759p24651759.html
Sent from the Lucene - Java Users mailing list archive at Nabble.com
KENIZED));
> then I indexed it and i ran the followed Similarity query to get the
> cosine similarity :
> query=SimilarityQueries.formSimilarQuery("this expression of
> galectin-1 in blood vessel walls was correlated with
> vascular",analyzer,"term",null);
the folow:
I created a doc:
doc.add(new Field("term","this expression of galectin-1 in blood
vessel walls was correlated with vascular",
Field.Store.YES,Field.Index.TOKENIZED));
then I indexed it and i ran the followed Similarity query to get the
cosin
od vessel walls
was correlated with vascular", Field.Store.YES,Field.Index.TOKENIZED));
then I indexed it and i ran the followed Similarity query to get the cosine
similarity :
query=SimilarityQueries.formSimilarQuery("this expression of galectin-1 in
blood vessel walls was correlate
What is SimilarityQueries? I'd try the explain capabilities to see
more.
On May 5, 2009, at 2:23 PM, Kamal Najib wrote:
hi all,
i got the similarity score 0.3044460713863373 between two docs which
have the same text content, is it correct? I expected 1.0, hier is
my result line:
doc:"
hi all,
i got the similarity score 0.3044460713863373 between two docs which have the
same text content, is it correct? I expected 1.0, hier is my result line:
doc:"this expression of galectin-1 in blood vessel walls was correlated with
vascular"
doc2 :"this expression of galectin-1 in blood v
Hi all,
I try to get the cosine similarity between two docs:
I have tried first to create a document for a String like this:
Document doc1=new Document();
doc1.add(new Field("term","nodular lesions over years responding kamal najib
nodular lesions over years responding&q
ms of performance)
way
to get the Cosine Similarity between two Lucene Documents.
I have seen that this can be done with:
1. Converting the document into a query and submitting the query,
getting
the results and their score. --TOO SLOW if you want this for all
documents
in a corpus
Hello *,
I have been trying to find an *efficient *(in terms of performance) way
to get the Cosine Similarity between two Lucene Documents.
I have seen that this can be done with:
1. Converting the document into a query and submitting the query, getting
the results and their score. --TOO
Have you looked at the MoreLikeThis class in the similarity package?
On 8/30/06, Winton Davies <[EMAIL PROTECTED]> wrote:
Hi All,
I'm scratching my head - can someone tell me which class implements
an efficient multiple term TF.IDF Cosine similarity scoring mechanism?
There is
Hi All,
I'm scratching my head - can someone tell me which class implements
an efficient multiple term TF.IDF Cosine similarity scoring mechanism?
There is clearly the single TermScorer - but I can't find the class
that would do a bucketed TF.IDF cosine - i.e. fill an accumulator
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