The attached test case demonstrates this problem and provides a fix:
1. Use a custom similarity to eliminate all tf and idf effects, just to
isolate what is being tested.
2. Create two documents doc1 and doc2, each with two fields title and
description. doc1 has "elephant" in title and "elephant" in description.
doc2 has "elephant" in title and "albino" in description.
3. Express query for "albino elephant" against both fields.
a. MultiFieldQueryParser won't recognize either document as containing
both terms, due to the way it expands the query across fields.
b. Expressing query as "title:albino description:albino title:elephant
description:elephant" will score both documents equivalently, since each
matches two query terms.
4. Comparison to MaxDisjunctionQuery and my method for expanding queries
across fields. Using notation that () represents a BooleanQuery and ( | )
represents a MaxDisjunctionQuery, "albino elephant" expands to:
( (title:albino | description:albino)
(title:elephant | description:elephant) )
This will recognize that doc2 has both terms matched while doc1 only has 1
term matched, score doc2 over doc1.
Refinement note: the actual expansion for "albino query" that I use is:
( (title:albino | description:albino)~0.1
(title:elephant | description:elephant)~0.1 )
This causes the score of each MaxDisjunctionQuery to be the score of highest
scoring MDQ subclause plus 0.1 times the sum of the scores of the other MDQ
subclauses. Thus, doc1 gets some credit for also having "elephant" in the
description but only 1/10 as much as doc2 gets for covering another query term
in its description. If doc3 has "elephant" in title and both "albino"
and "elephant" in the description, then with the actual refined expansion, it
gets the highest score of all (whereas with pure max, without the 0.1, it
would get the same score as doc2).
In real apps, tf's and idf's also come into play of course, but can affect
these either way (i.e., mitigate this fundamental problem or exacerbate it).