a question on possible search paths/structure. If we have a text
document, and we have run our magic over it and come away with Topics and
Entities (Like, Barack Obama and Apple Inc.) and we have a relevancy score
for each one, what would be the best way to store and query against them?
we currently are trying a parent/child relationship, where the children are
the terms with their relevancy score and the scoring of the parent text
document gets done from the relevancy scores of the children. That works.
Just worried about speed of parent/child against millions of documents.
Another way we could think of was, build our own scorer/analyzer. If we
are reading in tokens like BarackObama.93345|AppleInc.0034
where it has the topic and the relevancy score to the document in it, i can
build an analyzer to read those sorts of tokens, but is there any way to
build a scorer that can use that token match data to score?
and third, is there any other way to normalize this data into one document
so we can score on it. That seems like it would be the fastest way to
query, but my #2 option here is the only way I can think of doing it.
Anyone else tagging their documents with relevancy scores to topics, on the
document and then letting people search for those topics and pulling back
the relevant docs based on the per document relevancy scores?
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