Standard full text search with TF/IDF treats documents, or at least each field
within a document, as a big bag of words. The match query can tell us if
that bag contains our search terms or not, but that is only part of the story.
It can’t tell us anything about the relationship between words.
Consider the difference between these sentences:
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``Sue ate the alligator''
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``The alligator ate Sue''
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``Sue never goes anywhere without her alligator skin purse''
A match query for "sue alligator" would match all three documents, but it
doesn’t tell us if the two words form part of the same idea, or even the same
paragraph.
Understanding how words relate to each other is a very complicated problem and we are not going to be able to solve it by just using another type of query, but we can at least find words that appear to be related because they appear near to each other or even right next to each other.
Each document may be much longer than the examples we present above — Sue
and alligator may be separated by paragraphs of other text. Perhaps we still
want to return these documents where the words are widely separated, but we
want to give documents where the words are close together a higher relevance
score.
This is the province of phrase matching or proximity matching.
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Tip
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In this chapter we will be using the same example documents that we used for
the |