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Introduction to Information Retrieval Acknowledgements to Pandu Nayak and Prabhakar Raghavan of Stanford, Hinrich Schtze and Christina Lioma of Stutgart, Lee Giles (and his sources) of Penn State

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So far You have learned to crawl the web Being a good citizen and not hurting the servers Extracting the kind of information that you want Storing the retrieved material locally Now, what are you going to do with those materials? Develop a way to retrieve what you want from that collection, as you need it.

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Finding what you need in a collection of documents Information Retrieval: Given a collection of documents Retrieve One or more documents that contain the specific information you want Obtain the answer to an information need by querying the document collection

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Tonights class Introduce the concepts of Information Retrieval Assume you have a collection How to look into the documents for your information need How to do it efficiently

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Later Tools to build indices Apache Lucene Apache Solr

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A brief introduction to Information Retrieval Recall our primary resource: Christopher D. Manning, Prabhakar Raghavan and Hinrich Schtze, Introduction to Information Retrieval, Cambridge University Press. 2008. The entire book is available online, free, at http://nlp.stanford.edu/IR-book/information- retrieval-book.html http://nlp.stanford.edu/IR-book/information- retrieval-book.html I will use some of the slides that they provide to go with the book. I will also use slides from other sources and make new ones. The primary other source is Dr. Lee Giles, Penn State. Information Retrieval IST441

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Authors definition 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). Note the use of the word usually. We will see examples where the material is not documents, and not text.

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Examples and Scaling IR is about finding a needle in a haystack finding some particular thing in a very large collection of similar things. Our examples are necessarily small, so that we can comprehend them. Do remember, that all that we say must scale to very large quantities.

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Searching Shakespeare Which plays of Shakespeare contain the words Brutus AND Caesar but NOT Calpurnia? See http://www.rhymezone.com/shakespeare/http://www.rhymezone.com/shakespeare/ One could grep all of Shakespeares 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)

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Document match Go to http://www.rhymezone.com/shakespeare/ http://www.rhymezone.com/shakespeare/ Enter, one at a time, Caesar Brutus Calpurnia For each, note the collection of plays that contain the term We need to associate one or more plays with each term Term Incidence For our example, we will use a subset of the plays

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Term-document incidence 1 if play contains word, 0 otherwise Brutus AND Caesar BUT NOT Calpurnia All the plays All the terms First approach make a matrix with terms on one axis and plays on the other

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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 110111 AND 101111 = 100100. Reminder: 0 AND 0 = 0, 0 AND 1 =0, 1 AND 0 = 0, 1 AND 1 = 1 The effect of the AND operation is 1 if the items are both 1 and 0 if they are not both 1

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Answer 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.

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Spot check Try another one What is the vector for the query Antony and mercy What would we do to find Antony OR mercy?

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Basic assumptions about information retrieval Collection: Fixed set of documents Goal: Retrieve documents with information that is relevant to the users information need and helps the user complete a task

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The classic search model Corpus TASK Info Need Query Verbal form Results SEARCH ENGINE Query Refinement Ultimately, some task to perform. Some information is required in order to perform the task. The information need must be expressed in words (usually). The information need must be expressed in the form of a query that can be processed. It may be necessary to rephrase the query and try again

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The classic search model Corpus TASK Info Need Query Verbal form Results SEARCH ENGINE Query Refinement Get rid of mice in a politically correct way Info about removing mice without killing them How do I trap mice alive? mouse trap Misconception?Mistranslation?Misformulation? Potential pitfalls between task and query results

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How good are the results? Precision: How well do the results match the information need? Recall: What fraction of the available correct results were retrieved? These are the basic concepts of information retrieval evaluation.

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Size considerations 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.

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The matrix does not work 500K x 1M matrix has half-a- trillion 0s and 1s. But it has no more than one billion 1s. matrix is extremely sparse. Whats a better representation? We only record the 1 positions. i.e. We dont need to know which documents do not have a term, only those that do. Why?

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Inverted index For each term t, we must store a list of all documents that contain t. Identify each by a docID, a document serial number Can we used fixed-size arrays for this? Brutus Calpurnia Caesar 124561657132 124113145173174 2 3154 101 What happens if the word Caesar is added to document 14? More likely, add document 14, which contains Caesar.

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Inverted index We need variable-size postings lists On disk, a continuous run of postings is normal and best In memory, can use linked lists or variable length arrays Some tradeoffs in size/ease of insertion 22 Postings Sorted by docID (more later on why). Posting Brutus Calpurnia Caesar 124561657132 124113145173 231 174 54101

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Tokenizer Token stream. Friends RomansCountrymen Inverted index construction Linguistic modules Modified tokens. friend romancountryman Indexer Inverted index. friend roman countryman 24 2 13 16 1 Documents to be indexed. Friends, Romans, countrymen. Sec. 1.2 Stop words, stemming, capitalization, cases, etc.

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Indexer steps: Token sequence Sequence of (Modified token, Document ID) pairs. I did enact Julius Caesar I was killed i' the Capitol; Brutus killed me. Doc 1 So let it be with Caesar. The noble Brutus hath told you Caesar was ambitious Doc 2 Initially, all the tokens from document 1, then all the tokens from document 2, etc., without regard for duplication.

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Indexer steps: Sort Sort by terms docID within terms Core indexing step

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Indexer steps: Dictionary & Postings Multiple term entries in a single document are merged. Split into Dictionary and Postings Doc. frequency information is added. Number of documents in which the term appears ID of documents that contain the term

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Where do we pay in storage? 27 Pointers Terms and counts Lists of docIDs

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Storage A small diversion Computer storage Processor caches Main memory External storage (hard disk, other devices) Very substantial differences in access speeds Processor caches mostly used by the operating system for rapid access to data that will be needed soon Main memory. Limited quantities. High speed access Hard disk Much larger quantities, speed restricted, access in fixed units (blocks)

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Some size examples From the iMac Memory 4GB (two 2GB SO-DIMMs) of 1333MHz DDR3 SDRAM; four SO-DIMM slots support up to 16GB Hard drive 500GB or 1TB 7200-rpm Serial ATA hard drive Optional 2TB 7200-rpm Serial ATA hard drive These are big numbers, but the potential size of a significant collection is larger still. The steps taken to optimize use of storage are critical to satisfactory response time.

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Implications of size limits slot 1 slot 2 slot 3 slot 4 slot 5 slot 6 slot 7 slot 8 slot 9 slot 10 slot 11 slot 12 real memory (RAM) page 1 page 2 page 3 page 4... b page 70 page 71 page 72 virtual memory (on disk) reference to page 71 virtual page 71 is brought from disk into real memory virtual page 2 generates a page fault when referencing virtual page 71

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How do we process a query? Using the index we just built, examine the terms in some order, looking for the terms in the query.

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Query processing: AND Consider processing the query: Brutus AND Caesar Locate Brutus in the Dictionary; Retrieve its postings. Locate Caesar in the Dictionary; Retrieve its postings. Merge the two postings: 32 128 34 248163264123581321 Brutus Caesar

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The merge Walk through the two postings simultaneously, in time linear in the total number of postings entries 33 34 12824816 3264 12 3 581321 128 34 248163264123581321 Brutus Caesar 2 8 If the list lengths are x and y, the merge takes O(x+y) operations. What does that mean? Crucial: postings sorted by docID. Sec. 1.3

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Intersecting two postings lists (a merge algorithm) 34

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Spot Check Lets assume that the term mercy appears in documents 1, 2, 13, 18, 24,35, 54 the term noble appears in documents 1, 5, 7, 13, 22, 24, 56 Show the document lists, then step through the merge algorithm to obtain the search results.

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Boolean queries: Exact match The Boolean retrieval model is being able to ask a query that is a Boolean expression: Boolean Queries are queries using AND, OR and NOT to join query terms Views each document as a set of words Is precise: document matches condition or not. Perhaps the simplest model to build Primary commercial retrieval tool for 3 decades. Many search systems you still use are Boolean: Email, library catalog, Mac OS X Spotlight 36 Sec. 1.3

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37 Query optimization Consider a query that is an and of n terms, n > 2 For each of the terms, get its postings list, then and them together Example query: BRUTUS AND CALPURNIA AND CAESAR What is the best order for processing this query? 37

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38 Query optimization Example query: BRUTUS AND CALPURNIA AND CAESAR Simple and effective optimization: Process in order of increasing frequency Start with the shortest postings list, then keep cutting further In this example, first CAESAR, then CALPURNIA, then BRUTUS 38

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39 Optimized intersection algorithm for conjunctive queries 39

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40 More general optimization Example query: ( MADDING OR CROWD ) and ( IGNOBLE OR STRIFE ) Get frequencies for all terms Estimate the size of each or by the sum of its frequencies (conservative) Process in increasing order of or sizes 40

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Scaling These basic techniques are pretty simple There are challenges Scaling as everything becomes digitized, how well do the processes scale? Intelligent information extraction I want information, not just a link to a place that might have that information.

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How much information is there? Soon most everything will be recorded and indexed Most bytes will never be seen by humans. Data summarization, trend detection anomaly detection are key technologies See Mike Lesk: How much information is there: http://www.lesk.com/mlesk/ksg97/ksg.ht ml See Lyman & Varian: How much information http://www.sims.berkeley.edu/research/projects/how- much-info// Yotta Zetta Exa Peta Tera Giga Mega Kilo All books (words) All Books MultiMedia Everything Recorded ! A Photo 24 Yecto, 21 zepto, 18 atto, 15 femto, 12 pico, 9 nano, 6 micro, 3 milli Gray - Microsoft Slide source: Lee Giles (modified) A Book A movie

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Getting the required information Dependent on Acquiring information Storing information Indexing Interaction with the information source Evaluation

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Getting the required information We have learned about Acquiring information Storing information Indexing (the basics) Interaction with the information source Evaluation

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Getting the required information Still to come Acquiring information Storing information Indexing (more) Interaction with the information source Evaluation

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The web and its challenges Unusual and diverse documents Unusual and diverse users, queries, information needs Beyond terms, exploit ideas from social networks link analysis, clickstreams How do search engines work? And how can we make them better? 46

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References Christopher D. Manning, Prabhakar Raghavan and Hinrich Schtze, Introduction to Information Retrieval, Cambridge University Press. 2008. Book available online at http://nlp.stanford.edu/IR-book/information-retrieval-book.htmlhttp://nlp.stanford.edu/IR-book/information-retrieval-book.html Many of these slides are taken directly from the authors slides from the first chapter of the book. C. Lee Giles, Penn State. http://clgiles.ist.psu.edu/http://clgiles.ist.psu.edu/ Paging figure from Vittore Carsarosa, University of Parma, Italy