CS 572: Information Retrieval. Lecture 2: Hello World! (of Text Search)
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1 CS 572: Information Retrieval Lecture 2: Hello World! (of Text Search) 1/13/2016 CS 572: Information Retrieval. Spring
2 Course Logistics Lectures: Monday, Wed: 11:30am-12:45pm, W301 Following dates will be rescheduled or canceled: Feb 22, 24 (WSDM 2016); March 16 (CHIIR 2016); April 18. Office hours: Tuesdays 4-5pm (for now) Communication: Piazza (experiment): Course Website (minimalist version): 1/13/2016 CS 572: Information Retrieval. Spring
3 Logistics: Course structure Two parts: Part 1: (roughly through end of February): Fundamentals: indexing, retrieval, ranking, evaluation Part 2: Research topics in IR and Web search: Grading: Web, web 2.0, social networks, (more later) Two implementation homeworks: 30% Midterm exam: 25% Final project: 45% (proposal, implementation, presentation, report). 1/13/2016 CS 572: Information Retrieval. Spring
4 Texts BCC: Information Retrieval, Büttcher, Clarke, and Cormack, MIT Press: ($$) MRS: Introduction to Information Retrieval, Manning, Raghavan and Schütze, Cambridge University Press (free online): SUI: Search User interfaces, Marti Hearst, CUP, free online: Additional readings will be posted online as needed. 1/13/2016 CS 572: Information Retrieval. Spring
5 Information Retrieval Challenges Understand user s query (information need) Interpret and organize data (indexing) Rank documents by expected utility for user Find answers to show to user Evaluate, improve search, repeat 1/13/2016 CS 572: Information Retrieval. Spring
6 Current Focus: Indexing Source Selection Resource Query Formulation Query Search Ranked List Indexing Index Selection Documents Acquisition Collection Examination Documents Delivery 6
7 Users want instant response (gratification) IR Philosophy: Do maximum work at index time ( offline ), minimum work at query time Funny quote: Internet search engine Google can [quickly] scan a billion Web pages and find the one page that has the exact piece of information you are looking for. J. Surowiecki, Wisdom of Crowds. 7
8 First IR Task: Works of W. Shakespeare Corpus: Collective Plays of William Shakespeare 34,895 total speeches spoken by 1,223 characters. 884,421 total words in Shakespeare's 43 works. 28,829 unique word forms, and 12,493 occur only once. unique words account for 43.3% of total word forms. top 10 most frequent words: 21.4% of all words. top 100 most frequent words: 53.9% of all words. top 1% most frequent words: 66.7% of all words. Queries: Assume some combination of words Or don t assume. Look at past queries? William Shakespeare was an English poet, playwright, and actor, widely regarded as the greatest writer in the English language and the world's preeminent dramatist.
9 Sec. 1.1 Unstructured data in 1680 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) future lectures 9
10 Sec. 1.1 Term-document incidence matrix Antony and Cleopatra Julius Caesar The Tempest Hamlet Othello Macbeth Antony Brutus Caesar Calpurnia Cleopatra mercy worser Brutus AND Caesar BUT NOT Calpurnia 1 if play contains word, 0 otherwise
11 Sec. 1.1 Incidence vectors So we have a 0/1 vector for each term. Proposal for Boolean queries: To answer query: take the vectors for: Brutus, Caesar and Calpurnia (complemented) bitwise AND AND AND = Brutus AND Caesar BUT NOT Calpurnia 11
12 Sec. 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. 12
13 Simplifying assumptions (for now) Sec. 1.1 Collection: Fixed set of documents (to be revised) Goal: Retrieve documents with information that is relevant to the user s information need and helps the user complete a task need = Boolean query 13
14 Sec. 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. 500K x 1M matrix has half-a-trillion 0 s and 1 s (too big for RAM of most machines, even nowdays) Most are 0 s! Solution 1: efficient methods for storing and computing with sparse vectors. 14
15 Sparse Vectors as Lists Store vectors as linked lists of non-zero-weight tokens paired with a weight. Space proportional to number of unique tokens (n) in document. Requires linear search of the list to find (or change) the weight of a specific token. Requires quadratic time in worst case to compute vector for a document: n i= 1 i = n ( 2 n + 1) 2 = O( n ) 15
16 Sparse Vectors as Trees Index tokens in a document in a balanced binary tree or trie with weights stored with tokens at the leaves. < memory < < bit 2 film film 1 variable memory 1 variable 2 Balanced Binary Tree 16
17 Sparse Vectors as Trees (cont.) Space overhead for tree structure: ~2n nodes. O(log n) time to find or update weight of a specific token. O(n log n) time to construct vector. Need software package to support such data structures. 17
18 Sparse Vectors as HashTables Store tokens in hashtable, with token string as key and weight as value. Storage overhead for hashtable ~1.5n. Table must fit in main memory. Constant time to find or update weight of a specific token (ignoring collisions). O(n) time to construct vector (ignoring collisions). 18
19 Sec. 1.1 Alternative: Don t build the matrix! 500K x 1M matrix has half-a-trillion 0 s and 1 s. 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. Why? 19
20 Sec. 1.2 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 Caesar Calpurnia What happens if the word Caesar is added to document 14? 20
21 Sec. 1.2 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 Posting Brutus Caesar Calpurnia Dictionary Postings Sorted by docid (more later on why). 21
22 Sec. 1.2 Inverted index construction Documents to be indexed. Friends, Romans, countrymen. Tokenizer Token stream. Friends Romans Countrymen More on these later. Linguistic modules Modified tokens. friend roman countryman Indexer friend 2 4 Inverted index. roman countryman
23 Sec. 1.2 Indexer steps: Token sequence Sequence of (Modified token, Document ID) pairs. Doc 1 Doc 2 I did enact Julius Caesar I was killed i' the Capitol; Brutus killed me. So let it be with Caesar. The noble Brutus hath told you Caesar was ambitious
24 Sec. 1.2 Indexer steps: Sort Sort by terms And then docid Core indexing step
25 Sec. 1.2 Indexer steps: Dictionary & Postings Multiple term entries in a single document are merged. Split into Dictionary and Postings Doc. frequency information is added. Why frequency? Will discuss later.
26 Where do we pay in storage? Sec. 1.2 Lists of docids Terms and counts Later in the course: How do we index efficiently? How much storage do we need? Pointers 26
27 Sec. 1.3 The index we just built How do we process a query? Later - what kinds of queries can we process? 27
28 Sec. 1.3 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: Brutus Caesar 28
29 Sec. 1.3 The merge Walk through the two postings simultaneously, in time linear in the total number of postings entries Brutus Caesar If the list lengths are x and y, the merge takes O(x+y) operations. Crucial: postings sorted by docid. 29
30 Intersecting two postings lists (a merge algorithm) 30
31 Sec. 1.3 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 an IR system on Primary commercial retrieval tool for 3 decades. Many search systems you still use are Boolean: , library catalog, Mac OS X Spotlight 31
32 Sec. 1.4 Example: WestLaw Largest commercial (paying subscribers) legal search service (started 1975; ranking added 1992) Tens of terabytes of data; 700,000 users Majority of users still use boolean queries Example query: What is the statute of limitations in cases involving the federal tort claims act? LIMIT! /3 STATUTE ACTION /S FEDERAL /2 TORT /3 CLAIM /3 = within 3 words, /S = in same sentence 32
33 Sec. 1.4 Example: WestLaw Another example query: Requirements for disabled people to be able to access a workplace disabl! /p access! /s work-site work-place (employment /3 place Note that SPACE is disjunction (or), not conjunction (and)! Long, precise queries; proximity operators; incrementally developed; not like web search Many professional searchers still like Boolean search You know exactly what you are getting But that doesn t mean it actually works better.
34 Sec. 1.3 Boolean queries: More general merges Brutus Caesar Calpurnia Exercise: Adapt the merge algorithm for the queries: Brutus AND NOT Caesar Brutus OR NOT Caesar Can we still run through the merge in time O(x+y)? 34
35 Sec. 1.3 Merging What about an arbitrary Boolean formula? (Brutus OR Caesar) AND NOT (Antony OR Cleopatra) Can we always merge in linear time? Linear in terms of what? Can we do better? 35
36 Sec. 1.3 Query processing optimization What is the best order for query processing? Consider a query that is an AND of n terms. For each of the n terms, get its postings, then AND them together. Brutus Caesar Calpurnia Query: Brutus AND Calpurnia AND Caesar 36
37 Sec. 1.3 Query optimization example Process in order of increasing frequency: start with smallest set, then keep cutting further. This is why we kept document freq. in dictionary Brutus Caesar Calpurnia Execute the query as (Calpurnia AND Brutus) AND Caesar. 37
38 Sec. 1.3 More general optimization e.g., (madding OR crowd) AND (ignoble OR strife) Get doc. freq. s for all terms. Estimate the size of each OR by the sum of its doc. freq. s (conservative). Process in increasing order of OR sizes. 38
39 Exercise Optimize query processing for: (tangerine OR trees) AND (marmalade OR skies) AND (kaleidoscope OR eyes) Term Freq eyes kaleidoscope marmalade skies tangerine trees
40 More query processing exercises Exercise: If the query is friends AND romans AND (NOT countrymen), how could we use the freq of countrymen? Exercise: Extend the merge to an arbitrary Boolean query. Can we always guarantee execution in time linear in the total postings size? Hint: Begin with the case of a Boolean formula query: in this, each query term appears only once in the query. 40
41 Example of Inverted-List Implementation Try the search feature at Compare running time to OSS Write down five search features you think it could do better 41
42 First Project: Indexing and Ranking Source Selection Resource Query Formulation Query Search Ranked List Indexing Index Selection Documents Acquisition Collection Examination Documents Delivery 42
43 Project 1: Details will be announced next week Goals: implement a functional IR system using Lucene Extend baseline implementation by implementing classic ranking functions Corpus: newspaper articles Deliverables: Working baseline Lucene search implementation over TREC corpus Construct index over corpus Implement query processing for given query file Will extend ranking with functions introduced in class Your output input for empirical evaluation: Set of Lists of DOCIDs for each test query Program to compute performance will be provided 43
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