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    <p>Here's a really basic description; for more details, you can read this textbook (free online): <a href="http://informationretrieval.org/" rel="noreferrer">http://informationretrieval.org/</a>¹</p> <p>1). For all files, create an index. The index consists of all unique words that occur in your dataset (called a "corpus"). With each word, a list of document ids is associated; each document id refers to a document that contains the word.</p> <p>Variations: sometimes when you generate the index you want to ignore stop words ("a", "the", etc). You have to be careful, though ("to be or not to be" is a real query composed of stopwords).</p> <p>Sometimes you also stem the words. This has more impact on search quality in non-English languages that use suffixes and prefixes to a greater extent. </p> <p>2) When a user enters a query, look up the corresponding lists, and merge them. If it's a strict boolean query, the process is pretty straightforward -- for AND, a docid has to occur in all the word lists, for OR, in at least one wordlist, etc.</p> <p>3) If you want to rank your results, there are a number of ways to do that, but the basic idea is to use the frequency with which a word occurs in a document, as compared to the frequency you expect it to occur in any document in the corpus, as a signal that the document is more or less relevant. See textbook.</p> <p>4) You can also store word positions to infer phrases, etc.</p> <p>Most of that is irrelevant for desktop search, as you are more interested in recall (all documents that include the term) than ranking.</p> <hr> <p>¹ previously on <a href="http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html" rel="noreferrer">http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html</a>, accessible via wayback machine</p>
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