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@jemoka / Jemoka Knowledge Base / wiki/concepts/information_retrival.md
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--- title: "Information Retrival" type: concept related: [Ranked Information Retrieval, Information Retrival, Mean Average Precision] source: https://www.jemoka.com/posts/kbhinformation_retrival/ confidence: high status: active --- 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