Information Retrival

Information Retrival is trying to find material within large collections which is unstructured which satisfies an information need (of structured info).
Unstructured information has had a massive outburst after the millennium.
IMPORTANTLY: evaluating Information Retrival is based on Precision/Recall/F on information need and not the query.
For ranked system, we can come up with a curve of precision-recall curve by selecting increasing k, or mean average precision.
Basic Terminology collection a set of documents—could by static, or dynamically added
goal retrieve documents with information relevant to the user’s information need + to complete a task
information need information need is the actual information that is needed by a search; this is usually translated into a search query, which is actually used to search.
query query is a computer accessible form of text which searches to answer an information need.
information need: “info about removing mice without killing them” query: “trapping mouse alive” Stages of Interpolation user task => info need: we may not be looking for the right info info need => query: we may not be using the best methods to get the info we are looking for Motivation “what’s wrong with grepping?”
we cannot afford to do a linear search over web-scale data a “NOT” query is non-trivial no semantics we have no ranking, so we don’t know what’s the “best” document Ranked Approaches Ranked Information Retrieval