![Information scarcity vs. information abundance
Information scarcity problem (or needle-in-haystack problem):
hard to find rare information
Lord Byron’s first words? 3 years old? Long sentence to the
nurse in perfect English?
. . . when a servant had spilled an urn of hot coffee over his legs, he replied to
the distressed inquiries of the lady of the house, ’Thank you, madam, the
agony is somewhat abated.’ [not Lord Byron, but Lord Macaulay]
Information abundance problem (for more clear-cut
information needs): redundancy of obvious information
What is toxoplasmosis?
14](https://image.slidesharecdn.com/lecture1-220922063237-960abd1a/85/lecture1-pdf-15-320.jpg)
![The results: two documents
Antony and Cleopatra, Act III, Scene ii
Agrippa [Aside to Dominitus 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.
28](https://image.slidesharecdn.com/lecture1-220922063237-960abd1a/85/lecture1-pdf-30-320.jpg)
![Does Google use the Boolean Model?
On Google, the default interpretation of a query [w1 w2 ... wn] is
w1 AND w2 AND ... AND wn
Cases where you get hits which don’t contain one of the w−i:
Page contains variant of wi (morphology, misspelling,
synonym)
long query (n is large)
Boolean expression generates very few hits
wi was in the anchor text
Google also ranks the result set
Simple Boolean Retrieval returns matching documents in no
particular order.
Google (and most well-designed Boolean engines) rank hits
according to some estimator of relevance
41](https://image.slidesharecdn.com/lecture1-220922063237-960abd1a/85/lecture1-pdf-43-320.jpg)
lecture1.pdf
- 1. Lecture 1: Introduction and the Boolean Model Information Retrieval Computer Science Tripos Part II Simone Teufel Natural Language and Information Processing (NLIP) Group Simone.Teufel@cl.cam.ac.uk 1
- 2. Overview 1 Motivation Definition of “Information Retrieval” IR: beginnings to now 2 First Boolean Example Term-Document Incidence matrix The inverted index Processing Boolean Queries Practicalities of Boolean Search
- 3. What is Information Retrieval? Manning et al, 2008: Information retrieval (IR) is finding material . . . of an unstructured nature . . . that satisfies an information need from within large collections . . . . 2
- 4. What is Information Retrieval? Manning et al, 2008: Information retrieval (IR) is finding material . . . of an unstructured nature . . . that satisfies an information need from within large collections . . . . 3
- 6. Document Collections IR in the 17th century: Samuel Pepys, the famous English diarist, subject-indexed his treasured 1000+ books library with key words. 5
- 8. What we mean here by document collections Manning et al, 2008: Information retrieval (IR) is finding material (usually documents) of an unstructured nature . . . that satisfies an information need from within large collections (usually stored on computers). Document Collection: text units we have built an IR system over. Usually documents But could be memos book chapters paragraphs scenes of a movie turns in a conversation... Lots of them 7
- 9. IR Basics IR System Query Document Collection Set of relevant documents 8
- 10. IR Basics IR System Query web pages Set of relevant web pages 9
- 11. What is Information Retrieval? Manning et al, 2008: Information retrieval (IR) is finding material (usually documents) of an unstructured nature . . . that satisfies an information need from within large collections (usually stored on computers. 10
- 12. Structured vs Unstructured Data Unstructured data means that a formal, semantically overt, easy-for-computer structure is missing. In contrast to the rigidly structured data used in DB style searching (e.g. product inventories, personnel records) SELECT * FROM business catalogue WHERE category = ’florist’ AND city zip = ’cb1’ This does not mean that there is no structure in the data Document structure (headings, paragraphs, lists. . . ) Explicit markup formatting (e.g. in HTML, XML. . . ) Linguistic structure (latent, hidden) 11
- 13. Information Needs and Relevance Manning et al, 2008: 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). An information need is the topic about which the user desires to know more about. A query is what the user conveys to the computer in an attempt to communicate the information need. A document is relevant if the user perceives that it contains information of value with respect to their personal information need. 12
- 14. Types of information needs Manning et al, 2008: Information retrieval (IR) is finding material . . . of an unstructured nature . . . that satisfies an information need from within large collections . . . . Known-item search Precise information seeking search Open-ended search (“topical search”) 13
- 15. Information scarcity vs. information abundance Information scarcity problem (or needle-in-haystack problem): hard to find rare information Lord Byron’s first words? 3 years old? Long sentence to the nurse in perfect English? . . . when a servant had spilled an urn of hot coffee over his legs, he replied to the distressed inquiries of the lady of the house, ’Thank you, madam, the agony is somewhat abated.’ [not Lord Byron, but Lord Macaulay] Information abundance problem (for more clear-cut information needs): redundancy of obvious information What is toxoplasmosis? 14
- 16. Relevance Manning et al, 2008: 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). Are the retrieved documents about the target subject up-to-date? from a trusted source? satisfying the user’s needs? How should we rank documents in terms of these factors? More on this in a lecture soon 15
- 17. How well has the system performed? The effectiveness of an IR system (i.e., the quality of its search results) is determined by two key statistics about the system’s returned results for a query: Precision: What fraction of the returned results are relevant to the information need? Recall: What fraction of the relevant documents in the collection were returned by the system? What is the best balance between the two? Easy to get perfect recall: just retrieve everything Easy to get good precision: retrieve only the most relevant There is much more to say about this – lecture 5 16
- 18. IR today Web search ( ) Search ground are billions of documents on millions of computers issues: spidering; efficient indexing and search; malicious manipulation to boost search engine rankings Link analysis covered in Lecture 8 Enterprise and institutional search ( ) e.g company’s documentation, patents, research articles often domain-specific Centralised storage; dedicated machines for search. Most prevalent IR evaluation scenario: US intelligence analyst’s searches Personal information retrieval (email, pers. documents; ) e.g., Mac OS X Spotlight; Windows’ Instant Search Issues: different file types; maintenance-free, lightweight to run in background 17
- 19. A short history of IR 1945 1950s 1960s 1970s 1980s 1990s 2000s memex Term IR coined by Calvin Moers Literature searching systems; evaluation by P&R (Alan Kent) Cranfield experiments Boolean IR SMART 1 0 recall precision no items retrieved precision/ recall Salton; VSM pagerank TREC Multimedia Multilingual (CLEF) Recommendation Systems 18
- 20. IR for non-textual media 19
- 22. Areas of IR “Ad hoc” retrieval (lectures 1-5) web retrieval (lecture 8) Support for browsing and filtering document collections: Clustering (lecture 6) Classification; using fixed labels (common information needs, age groups, topics; lecture 7) Further processing a set of retrieved documents, e.g., by using natural language processing Information extraction Summarisation Question answering 21
- 23. Overview 1 Motivation Definition of “Information Retrieval” IR: beginnings to now 2 First Boolean Example Term-Document Incidence matrix The inverted index Processing Boolean Queries Practicalities of Boolean Search
- 24. Boolean Retrieval In the Boolean retrieval model we can pose any query in the form of a Boolean expression of terms i.e., one in which terms are combined with the operators and, or, and not. Shakespeare example 22
- 25. Brutus AND Caesar AND NOT Calpurnia Which plays of Shakespeare contain the words Brutus and Caesar, but not Calpurnia? Naive solution: linear scan through all text – “grepping” In this case, works OK (Shakespeare’s Collected works has less than 1M words). But in the general case, with much larger text colletions, we need to index. Indexing is an offline operation that collects data about which words occur in a text, so that at search time you only have to access the precompiled index. 23
- 26. The term-document incidence matrix Main idea: record for each document whether it contains each word out of all the different words Shakespeare used (about 32K). Antony and Julius Caesar The Tempest Hamlet Othello Macbeth Cleopatra 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 . . . Matrix element (t, d) is 1 if the play in column d contains the word in row t, 0 otherwise. 24
- 27. Query “Brutus AND Caesar AND NOT Calpunia” We compute the results for our query as the bitwise AND between vectors for Brutus, Caesar and complement (Calpurnia): Antony and Julius Caesar The Tempest Hamlet Othello Macbeth Cleopatra 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 . . . This returns two documents, “Antony and Cleopatra” and “Hamlet”. 25
- 28. Query “Brutus AND Caesar AND NOT Calpunia” We compute the results for our query as the bitwise AND between vectors for Brutus, Caesar and complement (Calpurnia): Antony and Julius Caesar The Tempest Hamlet Othello Macbeth Cleopatra Antony 1 1 0 0 0 1 Brutus 1 1 0 1 0 0 Caesar 1 1 0 1 1 1 ¬Calpurnia 1 0 1 1 1 1 Cleopatra 1 0 0 0 0 0 mercy 1 0 1 1 1 1 worser 1 0 1 1 1 0 . . . This returns two documents, “Antony and Cleopatra” and “Hamlet”. 26
- 29. Query “Brutus AND Caesar AND NOT Calpunia” We compute the results for our query as the bitwise AND between vectors for Brutus, Caesar and complement (Calpurnia): Antony and Julius Caesar The Tempest Hamlet Othello Macbeth Cleopatra Antony 1 1 0 0 0 1 Brutus 1 1 0 1 0 0 Caesar 1 1 0 1 1 1 ¬Calpurnia 1 0 1 1 1 1 Cleopatra 1 0 0 0 0 0 mercy 1 0 1 1 1 1 worser 1 0 1 1 1 0 AND 1 0 0 1 0 0 Bitwise AND returns two documents, “Antony and Cleopatra” and “Hamlet”. 27
- 30. The results: two documents Antony and Cleopatra, Act III, Scene ii Agrippa [Aside to Dominitus 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. 28
- 31. Bigger collections Consider N=106 documents, each with about 1000 tokens 109 tokens at avg 6 Bytes per token ⇒ 6GB Assume there are M=500,000 distinct terms in the collection Size of incidence matrix is then 500,000 ×106 Half a trillion 0s and 1s 29
- 32. Can’t build the Term-Document incidence matrix Observation: the term-document matrix is very sparse Contains no more than one billion 1s. Better representation: only represent the things that do occur Term-document matrix has other disadvantages, such as lack of support for more complex query operators (e.g., proximity search) We will move towards richer representations, beginning with the inverted index. 30
- 33. The inverted index The inverted index consists of a dictionary of terms (also: lexicon, vocabulary) and a postings list for each term, i.e., a list that records which documents the term occurs in. Brutus 1 2 4 45 31 11 174 173 Caesar 132 1 2 4 5 6 16 57 Calpurnia 54 101 2 31 179 31
- 34. Processing Boolean Queries: conjunctive queries Our Boolean Query Brutus AND Calpurnia Locate the postings lists of both query terms and intersect them. Brutus 1 2 4 45 31 11 174 173 54 101 2 31 Calpurnia Intersection 2 31 Note: this only works if postings lists are sorted 32
- 35. Algorithm for intersection of two postings INTERSECT (p1, p2) 1 answer ← <> 2 while p1 6= NIL and p2 6= NIL 3 do if docID(p1) = docID(p2) 4 then ADD (answer, docID(p1)) 5 p1 ← next(p1) 6 p2 ← next(p2) 7 if docID(p1) < docID(p2) 8 then p1← next(p1) 9 else p2← next(p2) 10 return answer Brutus 1 2 4 45 31 11 174 173 54 101 2 31 Calpurnia Intersection 2 31 33
- 36. Complexity of the Intersection Algorithm Bounded by worst-case length of postings lists Thus “officially” O(N), with N the number of documents in the document collection But in practice much, much better than linear scanning, which is asymptotically also O(N) 34
- 37. Query Optimisation: conjunctive terms Organise order in which the postings lists are accessed so that least work needs to be done Brutus AND Caesar AND Calpurnia Process terms in increasing document frequency: execute as (Calpurnia AND Brutus) AND Caesar Brutus 1 2 4 45 31 11 174 173 Caesar 132 1 2 4 5 6 16 57 Calpurnia 54 101 2 31 8 9 4 179 35
- 38. Query Optimisation: disjunctive terms (maddening OR crowd) AND (ignoble OR strife) AND (killed OR slain) Process the query in increasing order of the size of each disjunctive term Estimate this in turn (conservatively) by the sum of frequencies of its disjuncts 36
- 39. Practical Boolean Search Provided by large commercial information providers 1960s-1990s Complex query language; complex and long queries Extended Boolean retrieval models with additional operators – proximity operators Proximity operator: two terms must occur close together in a document (in terms of certain number of words, or within sentence or paragraph) Unordered results... 37
- 40. Example: Westlaw Largest commercial legal search service – 500K subscribers Boolean Search and ranked retrieval both offered Document ranking only wrt chronological order Expert queries are carefully defined and incrementally developed 38
- 41. Westlaw Queries/Information Needs “trade secret” /s disclos! /s prevent /s employe! Information need: Information on the legal theories involved in preventing the disclosure of trade secrets by employees formerly employed by a competing company. disab! /p access! /s work-site work-place (employment /3 place) Information need: Requirements for disabled people to be able to access a workplace. host! /p (responsib! liab!) /p (intoxicat! drunk!) /p guest Information need: Cases about a host’s responsibility for drunk guests. 39
- 42. Comments on WestLaw Proximity operators: /3= within 3 words, /s=within same sentence /p =within a paragraph Space is disjunction, not conjunction (This was standard in search pre-Google.) Long, precise queries: incrementally developed, unlike web search Why professional searchers like Boolean queries: precision, transparency, control. 40
- 43. Does Google use the Boolean Model? On Google, the default interpretation of a query [w1 w2 ... wn] is w1 AND w2 AND ... AND wn Cases where you get hits which don’t contain one of the w−i: Page contains variant of wi (morphology, misspelling, synonym) long query (n is large) Boolean expression generates very few hits wi was in the anchor text Google also ranks the result set Simple Boolean Retrieval returns matching documents in no particular order. Google (and most well-designed Boolean engines) rank hits according to some estimator of relevance 41
- 44. Reading Manning, Raghavan, Schütze: Introduction to Information Retrieval (MRS), chapter 1 42