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Introducing Information Retrieval and Web Search

Introducing Information Retrieval and Web Search
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WilliamsMcmahon,United States,Professional
Published Date:20-07-2017
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Introduction to Information Retrieval Introducing Information Retrieval and Web Search www.ThesisScientist.comInformation Retrieval • Information Retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers). – These days we frequently think first of web search, but there are many other cases: • E-mail search • Searching your laptop • Corporate knowledge bases • Legal information retrieval www.ThesisScientist.comUnstructured (text) vs. structured (database) data in the mid-nineties www.ThesisScientist.comUnstructured (text) vs. structured (database) data today www.ThesisScientist.comSec. 1.1 Basic assumptions of Information Retrieval • Collection: A set of documents – Assume it is a static collection for the moment • Goal: Retrieve documents with information that is relevant to the user’s information need and helps the user complete a task www.ThesisScientist.comThe classic search model Get rid of mice in a User task politically correct way Misconception? Info about removing mice Info need without killing them Misformulation? Query Searc how trap mice alive h Search engine Query Results Collection refinement www.ThesisScientist.comSec. 1.1 How good are the retrieved docs?  Precision : Fraction of retrieved docs that are relevant to the user’s information need  Recall : Fraction of relevant docs in collection that are retrieved  More precise definitions and measurements to follow later www.ThesisScientist.comIntroduction to Information Retrieval Term-document incidence matrices www.ThesisScientist.comSec. 1.1 Unstructured data in 1620 • Which plays of Shakespeare contain the words Brutus AND Caesar but NOT Calpurnia? • One could grep all of Shakespeare’s plays for Brutus and Caesar, then strip out lines containing Calpurnia? • Why is that not the answer? – Slow (for large corpora) – NOT Calpurnia is non-trivial – Other operations (e.g., find the word Romans near countrymen) not feasible – Ranked retrieval (best documents to return) • Later lectures www.ThesisScientist.comSec. 1.1 Term-document incidence matrices Antony and Cleopatra Julius Caesar The Tempest Hamlet Othello Macbeth Antony 1 1 0 0 0 1 Brutus 1 1 0 1 0 0 Caesar 1 1 0 1 1 1 Calpurnia 0 1 0 0 0 0 Cleopatra 1 0 0 0 0 0 mercy 1 0 1 1 1 1 worser 1 0 1 1 1 0 1 if play contains Brutus AND Caesar BUT NOT word, 0 otherwise Calpurnia www.ThesisScientist.comSec. 1.1 Incidence vectors • So we have a 0/1 vector for each term. • To answer query: take the vectors for Brutus, Caesar and Calpurnia (complemented)  bitwise AND. – 110100 AND Antony and Cleopatra Julius Caesar The Tempest Hamlet Othello Macbeth – 110111 AND Antony 1 1 0 0 0 1 Brutus 1 1 0 1 0 0 Caesar 1 1 0 1 1 1 – 101111 = Calpurnia 0 1 0 0 0 0 Cleopatra 1 0 0 0 0 0 mercy 1 0 1 1 1 1 – 100100 worser 1 0 1 1 1 0 www.ThesisScientist.comSec. 1.1 Answers to query • Antony and Cleopatra, Act III, Scene ii Agrippa Aside to DOMITIUS ENOBARBUS: Why, Enobarbus, When Antony found Julius Caesar dead, He cried almost to roaring; and he wept When at Philippi he found Brutus slain. • Hamlet, Act III, Scene ii Lord Polonius: I did enact Julius Caesar I was killed i’ the Capitol; Brutus killed me. www.ThesisScientist.comSec. 1.1 Bigger collections • Consider N = 1 million documents, each with about 1000 words. • Avg 6 bytes/word including spaces/punctuation – 6GB of data in the documents. • Say there are M = 500K distinct terms among these. www.ThesisScientist.comSec. 1.1 Can’t build the matrix • 500K x 1M matrix has half-a-trillion 0’s and 1’s. Why? • But it has no more than one billion 1’s. – matrix is extremely sparse. • What’s a better representation? – We only record the 1 positions. www.ThesisScientist.comIntroduction to Information Retrieval The Inverted Index The key data structure underlying modern IR www.ThesisScientist.comSec. 1.2 Inverted index • For each term t, we must store a list of all documents that contain t. – Identify each doc by a docID, a document serial number • Can we used fixed-size arrays for this? Brutus 1 2 4 11 31 45173174 Caesar 1 2 4 5 6 16 57 132 Calpurnia 2 31 54101 What happens if the word Caesar is added to document 14? www.ThesisScientist.comSec. 1.2 Inverted index • We need variable-size postings lists – On disk, a continuous run of postings is normal and best Posting – In memory, can use linked lists or variable length arrays Brutus 1 2 4 11 31 45173174 • Some tradeoffs in size/ease of insertion Caesar 1 2 4 5 6 16 57 132 Calpurnia 2 31 54101 Dictionary Postings www.ThesisScientist.com Sorted by docID (more later on why).Sec. 1.2 Inverted index construction Documents to Friends, Romans, countrymen. be indexed Tokenizer Friends Token stream Romans Countrymen Linguistic modules friend roman countryman Modified tokens Indexer friend 2 4 roman 1 2 Inverted index countryman 16 www.ThesisScientist.com 13Initial stages of text processing • Tokenization – Cut character sequence into word tokens • Deal with “John’s”, a state-of-the-art solution • Normalization – Map text and query term to same form • You want U.S.A. and USA to match • Stemming – We may wish different forms of a root to match • authorize, authorization • Stop words – We may omit very common words (or not) • the, a, to, of www.ThesisScientist.comSec. 1.2 Indexer steps: Token sequence • Sequence of (Modified token, Document ID) pairs. Doc 1 Doc 2 I did enact Julius So let it be with Caesar I was killed Caesar. The noble i’ the Capitol; Brutus hath told you Brutus killed me. Caesar was ambitious www.ThesisScientist.com