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https://issues.apache.org/jira/browse/LUCENE-6954?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15191248#comment-15191248
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Anshum Gupta commented on LUCENE-6954:
--------------------------------------
Hi Alessandro, I really want to take a look at this but there's a lot on my
plate at the moment. I'll try and look at this next week if no one else gets to
it.
> More Like This Query Generation
> --------------------------------
>
> Key: LUCENE-6954
> URL: https://issues.apache.org/jira/browse/LUCENE-6954
> Project: Lucene - Core
> Issue Type: Improvement
> Components: modules/other
> Affects Versions: 5.4
> Reporter: Alessandro Benedetti
> Labels: morelikethis
> Attachments: LUCENE-6954.patch
>
>
> Currently the query is generated :
> org.apache.lucene.queries.mlt.MoreLikeThis#retrieveTerms(int)
> 1) we extract the terms from the interesting fields, adding them to a map :
> Map<String, Int> termFreqMap = new HashMap<>();
> ( we lose the relation field-> term, we don't know anymore where the term was
> coming ! )
> org.apache.lucene.queries.mlt.MoreLikeThis#createQueue
> 2) we build the queue that will contain the query terms, at this point we
> connect again there terms to some field, but :
> ...
> // go through all the fields and find the largest document frequency
> String topField = fieldNames[0];
> int docFreq = 0;
> for (String fieldName : fieldNames) {
> int freq = ir.docFreq(new Term(fieldName, word));
> topField = (freq > docFreq) ? fieldName : topField;
> docFreq = (freq > docFreq) ? freq : docFreq;
> }
> ...
> We identify the topField as the field with the highest document frequency for
> the term t .
> Then we build the termQuery :
> queue.add(new ScoreTerm(word, topField, score, idf, docFreq, tf));
> In this way we lose a lot of precision.
> Not sure why we do that.
> I would prefer to keep the relation between terms and fields.
> The MLT query can improve a lot the quality.
> If i run the MLT on 2 fields : weSell and weDontSell for example.
> It is likely I want to find documents with similar terms in the weSell and
> similar terms in the weDontSell, without mixing up the things and loosing the
> semantic of the terms.
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