Introduction to Information Retrieval
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1 Introduction to Information Retrieval Christof Monz and Maarten de Rijke Spring 2002 Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 1
2 Today s Program Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 2
3 Today s Program What s Information Retrieval? Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 2
4 Today s Program What s Information Retrieval? Some administrative stuff Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 2
5 Today s Program What s Information Retrieval? Some administrative stuff Overview of the course Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 2
6 Today s Program What s Information Retrieval? Some administrative stuff Overview of the course Grading, homework etc. Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 2
7 Today s Program What s Information Retrieval? Some administrative stuff Overview of the course Grading, homework etc. How to represent information Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 2
8 Today s Program What s Information Retrieval? Some administrative stuff Overview of the course Grading, homework etc. How to represent information Our first retrieval model: boolean retrieval Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 2
9 What is Information Retrieval? Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
10 What is Information Retrieval? Finding relevant information in large collections of data Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
11 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
12 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Give me information on the history of the Kennedys Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
13 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Give me information on the history of the Kennedys An article about the Kennedys Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
14 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Give me information on the history of the Kennedys An article about the Kennedys (text retrieval) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
15 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Give me information on the history of the Kennedys An article about the Kennedys (text retrieval) What does a brain tumor look like on a CT-scan Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
16 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Give me information on the history of the Kennedys An article about the Kennedys (text retrieval) What does a brain tumor look like on a CT-scan A picture of a brain tumor Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
17 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Give me information on the history of the Kennedys An article about the Kennedys (text retrieval) What does a brain tumor look like on a CT-scan A picture of a brain tumor (image retrieval) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
18 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Give me information on the history of the Kennedys An article about the Kennedys (text retrieval) What does a brain tumor look like on a CT-scan A picture of a brain tumor (image retrieval) It goes like this: hmm hmm hahmmm... Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
19 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Give me information on the history of the Kennedys An article about the Kennedys (text retrieval) What does a brain tumor look like on a CT-scan A picture of a brain tumor (image retrieval) It goes like this: hmm hmm hahmmm... A certain song Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
20 What is Information Retrieval? Finding relevant information in large collections of data In such a collection you may want to find: Give me information on the history of the Kennedys An article about the Kennedys (text retrieval) What does a brain tumor look like on a CT-scan A picture of a brain tumor (image retrieval) It goes like this: hmm hmm hahmmm... A certain song (music retrieval) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 3
21 Text Retrieval Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 4
22 Text Retrieval Online library catalogs (OPAC) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 4
23 Text Retrieval Online library catalogs (OPAC) Internet search engines, such as AltaVista, Google, Ilse Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 4
24 Text Retrieval Online library catalogs (OPAC) Internet search engines, such as AltaVista, Google, Ilse Specialized systems (aka vendors): Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 4
25 Text Retrieval Online library catalogs (OPAC) Internet search engines, such as AltaVista, Google, Ilse Specialized systems (aka vendors): MEDLINE (medical articles) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 4
26 Text Retrieval Online library catalogs (OPAC) Internet search engines, such as AltaVista, Google, Ilse Specialized systems (aka vendors): MEDLINE (medical articles) Lexis-Nexis (legal, business, academic,... ) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 4
27 Text Retrieval Online library catalogs (OPAC) Internet search engines, such as AltaVista, Google, Ilse Specialized systems (aka vendors): MEDLINE (medical articles) Lexis-Nexis (legal, business, academic,... ) Westlaw (legal articles) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 4
28 Text Retrieval Online library catalogs (OPAC) Internet search engines, such as AltaVista, Google, Ilse Specialized systems (aka vendors): MEDLINE (medical articles) Lexis-Nexis (legal, business, academic,... ) Westlaw (legal articles) Dialog (business information) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 4
29 Retrieval vs. Browsing Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 5
30 Retrieval vs. Browsing Popular Web Directories: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 5
31 Retrieval vs. Browsing Popular Web Directories: Yahoo!, Open Directory Project (dmoz) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 5
32 Retrieval vs. Browsing Popular Web Directories: Yahoo!, Open Directory Project (dmoz) The user has to guess the right directories to find the information Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 5
33 Retrieval vs. Browsing Popular Web Directories: Yahoo!, Open Directory Project (dmoz) The user has to guess the right directories to find the information The user has to adapt to the designers conceptualization of the directory Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 5
34 Retrieval vs. Browsing Popular Web Directories: Yahoo!, Open Directory Project (dmoz) The user has to guess the right directories to find the information The user has to adapt to the designers conceptualization of the directory The goal of information retrieval is to provide immediate random access to the data Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 5
35 Retrieval vs. Browsing Popular Web Directories: Yahoo!, Open Directory Project (dmoz) The user has to guess the right directories to find the information The user has to adapt to the designers conceptualization of the directory The goal of information retrieval is to provide immediate random access to the data The user can specifiy his information need Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 5
36 IR vs. Database Querying Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 6
37 IR vs. Database Querying IR is not the same thing as querying a database Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 6
38 IR vs. Database Querying IR is not the same thing as querying a database Database querying assumes that the data is in a standardized format Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 6
39 IR vs. Database Querying IR is not the same thing as querying a database Database querying assumes that the data is in a standardized format Transforming all information, news articles, web sites into a database format is difficult and impossible for large data collections Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 6
40 IR vs. Database Querying IR is not the same thing as querying a database Database querying assumes that the data is in a standardized format Transforming all information, news articles, web sites into a database format is difficult and impossible for large data collections Text retrieval can work with plain, unformatted data Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 6
41 Relevance as Similarity Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 7
42 Relevance as Similarity A fundamental idea within IR is: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 7
43 Relevance as Similarity A fundamental idea within IR is: A document is relevant to a query if they are similar Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 7
44 Relevance as Similarity A fundamental idea within IR is: A document is relevant to a query if they are similar Similarity can be defined as Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 7
45 Relevance as Similarity A fundamental idea within IR is: A document is relevant to a query if they are similar Similarity can be defined as string matching/comparison Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 7
46 Relevance as Similarity A fundamental idea within IR is: A document is relevant to a query if they are similar Similarity can be defined as string matching/comparison similar vocabulary Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 7
47 Relevance as Similarity A fundamental idea within IR is: A document is relevant to a query if they are similar Similarity can be defined as string matching/comparison similar vocabulary same meaning of text Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 7
48 The Ubiquity of IR Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 8
49 The Ubiquity of IR Information filtering Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 8
50 The Ubiquity of IR Information filtering routing Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 8
51 The Ubiquity of IR Information filtering routing Text categorization Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 8
52 The Ubiquity of IR Information filtering routing Text categorization Detecting information structure Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 8
53 The Ubiquity of IR Information filtering routing Text categorization Detecting information structure Hyperlink generation Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 8
54 The Ubiquity of IR Information filtering routing Text categorization Detecting information structure Hyperlink generation Topic/Information detection/screening Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 8
55 The Ubiquity of IR Information filtering routing Text categorization Detecting information structure Hyperlink generation Topic/Information detection/screening Portal development and maintenance Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 8
56 The Ubiquity of IR Information filtering routing Text categorization Detecting information structure Hyperlink generation Topic/Information detection/screening Portal development and maintenance Question Answering Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 8
57 Some Research Groups in IR Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 9
58 Some Research Groups in IR Industrial IR research: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 9
59 Some Research Groups in IR Industrial IR research: AT&T, NEC, Sun Microsystems, Microsoft, G&E Research, Sabir Research, NTT, AltaVista, Xerox, Q-Go, GO.com (Infoseek), Lexiquest, Answers.com, AnswerLogics, Google, Ask-Jeeves, Lucent Technologies, IBM... Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 9
60 Some Research Groups in IR Industrial IR research: AT&T, NEC, Sun Microsystems, Microsoft, G&E Research, Sabir Research, NTT, AltaVista, Xerox, Q-Go, GO.com (Infoseek), Lexiquest, Answers.com, AnswerLogics, Google, Ask-Jeeves, Lucent Technologies, IBM... Academic IR Groups: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 9
61 Some Research Groups in IR Industrial IR research: AT&T, NEC, Sun Microsystems, Microsoft, G&E Research, Sabir Research, NTT, AltaVista, Xerox, Q-Go, GO.com (Infoseek), Lexiquest, Answers.com, AnswerLogics, Google, Ask-Jeeves, Lucent Technologies, IBM... Academic IR Groups: Cornell, Massachusetts, Twente, Glasgow, Sheffield, Dortmund, Dublin, Stanford, Syracruse, Virginia Tech, Pisa... Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 9
62 Some Research Groups in IR Industrial IR research: AT&T, NEC, Sun Microsystems, Microsoft, G&E Research, Sabir Research, NTT, AltaVista, Xerox, Q-Go, GO.com (Infoseek), Lexiquest, Answers.com, AnswerLogics, Google, Ask-Jeeves, Lucent Technologies, IBM... Academic IR Groups: Cornell, Massachusetts, Twente, Glasgow, Sheffield, Dortmund, Dublin, Stanford, Syracruse, Virginia Tech, Pisa... Other: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 9
63 Some Research Groups in IR Industrial IR research: AT&T, NEC, Sun Microsystems, Microsoft, G&E Research, Sabir Research, NTT, AltaVista, Xerox, Q-Go, GO.com (Infoseek), Lexiquest, Answers.com, AnswerLogics, Google, Ask-Jeeves, Lucent Technologies, IBM... Academic IR Groups: Cornell, Massachusetts, Twente, Glasgow, Sheffield, Dortmund, Dublin, Stanford, Syracruse, Virginia Tech, Pisa... Other: CIA, DARPA, ERCIM, Mitre, NIST... Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 9
64 History of IR Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 10
65 History of IR 1950: Calvin N. Moors coins the term Information Retrieval Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 10
66 History of IR 1950: Calvin N. Moors coins the term Information Retrieval 1959: Luhn describes statistical retrieval Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 10
67 History of IR 1950: Calvin N. Moors coins the term Information Retrieval 1959: Luhn describes statistical retrieval 1960: Maron and Kuhns define a probabilistic model of IR Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 10
68 History of IR 1950: Calvin N. Moors coins the term Information Retrieval 1959: Luhn describes statistical retrieval 1960: Maron and Kuhns define a probabilistic model of IR 1966: Cranfield project defines evaluation measures Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 10
69 History of IR 1950: Calvin N. Moors coins the term Information Retrieval 1959: Luhn describes statistical retrieval 1960: Maron and Kuhns define a probabilistic model of IR 1966: Cranfield project defines evaluation measures 1968: Gerard Salton s first book about the SMART retrieval system Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 10
70 History of IR 1950: Calvin N. Moors coins the term Information Retrieval 1959: Luhn describes statistical retrieval 1960: Maron and Kuhns define a probabilistic model of IR 1966: Cranfield project defines evaluation measures 1968: Gerard Salton s first book about the SMART retrieval system 1972: Lockheed introduces DIALOG as commercial online service Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 10
71 History of IR 1950: Calvin N. Moors coins the term Information Retrieval 1959: Luhn describes statistical retrieval 1960: Maron and Kuhns define a probabilistic model of IR 1966: Cranfield project defines evaluation measures 1968: Gerard Salton s first book about the SMART retrieval system 1972: Lockheed introduces DIALOG as commercial online service Late 1980 s: First PC systems incorporate retrieval Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 10
72 History of IR Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 11
73 History of IR Early 1990 s: Cheap disks lead to the information storage revolution Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 11
74 History of IR Early 1990 s: Cheap disks lead to the information storage revolution 1992: Westlaw is the first large-scale information service using probabilistic retrieval Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 11
75 History of IR Early 1990 s: Cheap disks lead to the information storage revolution 1992: Westlaw is the first large-scale information service using probabilistic retrieval Mid 1990 s: Multi-media databases Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 11
76 History of IR Early 1990 s: Cheap disks lead to the information storage revolution 1992: Westlaw is the first large-scale information service using probabilistic retrieval Mid 1990 s: Multi-media databases 1994: The internet and web explosion Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 11
77 History of IR Early 1990 s: Cheap disks lead to the information storage revolution 1992: Westlaw is the first large-scale information service using probabilistic retrieval Mid 1990 s: Multi-media databases 1994: The internet and web explosion 1995: IR techniques are incorporated in all kinds of information management applications Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 11
78 Overview of the Course Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 12
79 Overview of the Course Basic IR models (week 1 & 2) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 12
80 Overview of the Course Basic IR models (week 1 & 2) Evaluating the quality of IR methods (week 3) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 12
81 Overview of the Course Basic IR models (week 1 & 2) Evaluating the quality of IR methods (week 3) Text representation (week 4) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 12
82 Overview of the Course Basic IR models (week 1 & 2) Evaluating the quality of IR methods (week 3) Text representation (week 4) Components of an IR system (week 5 & 6) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 12
83 Overview of the Course Basic IR models (week 1 & 2) Evaluating the quality of IR methods (week 3) Text representation (week 4) Components of an IR system (week 5 & 6) Improving effectiveness and efficiency (week 6 & 7) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 12
84 Overview of the Course Basic IR models (week 1 & 2) Evaluating the quality of IR methods (week 3) Text representation (week 4) Components of an IR system (week 5 & 6) Improving effectiveness and efficiency (week 6 & 7) Web-based IR (week 8 & 9) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 12
85 Overview of the Course Basic IR models (week 1 & 2) Evaluating the quality of IR methods (week 3) Text representation (week 4) Components of an IR system (week 5 & 6) Improving effectiveness and efficiency (week 6 & 7) Web-based IR (week 8 & 9) Current research themes (week 10) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 12
86 Objectives of the Course Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 13
87 Objectives of the Course At the end of the course you will be able to... Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 13
88 Objectives of the Course At the end of the course you will be able to... Exploit web specific information when searching Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 13
89 Objectives of the Course At the end of the course you will be able to... Exploit web specific information when searching Understand the core components of modern IR systems Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 13
90 Objectives of the Course At the end of the course you will be able to... Exploit web specific information when searching Understand the core components of modern IR systems Understand the potential of IR techniques for today s information society Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 13
91 Objectives of the Course At the end of the course you will be able to... Exploit web specific information when searching Understand the core components of modern IR systems Understand the potential of IR techniques for today s information society Build your own search engine (in principle) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 13
92 Objectives of the Course At the end of the course you will be able to... Exploit web specific information when searching Understand the core components of modern IR systems Understand the potential of IR techniques for today s information society Build your own search engine (in principle) Make some serious dough Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 13
93 Grading etc. Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 14
94 Grading etc. Prerequisites: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 14
95 Prerequisites: Grading etc. Computer literacy (including an account on gene plus the ability to use the unix command line interface) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 14
96 Prerequisites: Grading etc. Computer literacy (including an account on gene plus the ability to use the unix command line interface) Assessment: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 14
97 Prerequisites: Grading etc. Computer literacy (including an account on gene plus the ability to use the unix command line interface) Assessment: Weekly reading assignments (1 or 2 papers per week) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 14
98 Prerequisites: Grading etc. Computer literacy (including an account on gene plus the ability to use the unix command line interface) Assessment: Weekly reading assignments (1 or 2 papers per week) (3-5) assignments Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 14
99 Prerequisites: Grading etc. Computer literacy (including an account on gene plus the ability to use the unix command line interface) Assessment: Weekly reading assignments (1 or 2 papers per week) (3-5) assignments Final exam Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 14
100 Prerequisites: Grading etc. Computer literacy (including an account on gene plus the ability to use the unix command line interface) Assessment: Weekly reading assignments (1 or 2 papers per week) (3-5) assignments Final exam Final mark is obtained as the average of the final exam (60%), assignments (30%) and reading (10%) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 14
101 Web Site of the Course Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 15
102 Web Site of the Course URL: christof/courses/ir/ Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 15
103 Web Site of the Course URL: christof/courses/ir/ Features of the web site: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 15
104 Web Site of the Course URL: christof/courses/ir/ Features of the web site: Some of the reading material is available online Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 15
105 Web Site of the Course URL: christof/courses/ir/ Features of the web site: Some of the reading material is available online Links to universities, companies and people relevant to IR Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 15
106 Web Site of the Course URL: christof/courses/ir/ Features of the web site: Some of the reading material is available online Links to universities, companies and people relevant to IR Printer-friendly versions of the transparancies Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 15
107 Web Site of the Course URL: christof/courses/ir/ Features of the web site: Some of the reading material is available online Links to universities, companies and people relevant to IR Printer-friendly versions of the transparancies Fill out the online form to be added to the mailing list for this course (important!) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 15
108 Retrieval Models Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 16
109 Retrieval Models A retrieval model is an idealization or abstraction of an actual retrieval process Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 16
110 Retrieval Models A retrieval model is an idealization or abstraction of an actual retrieval process Conclusions derived from a model depend on whether the model is a good approximation of the retrieval situation Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 16
111 Retrieval Models A retrieval model is an idealization or abstraction of an actual retrieval process Conclusions derived from a model depend on whether the model is a good approximation of the retrieval situation Note that a retrieval model is not the same thing as a retrieval implementation Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 16
112 Retrieval Models Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 17
113 Retrieval Models document representations User identify relevant information query formulation display documents to the user Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 17
114 Components of a Retrieval Model Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 18
115 Components of a Retrieval Model The user: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 18
116 Components of a Retrieval Model The user: Search expert (e.g., librarian) vs. non-expert Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 18
117 Components of a Retrieval Model The user: Search expert (e.g., librarian) vs. non-expert Backgound of the user (knowledge of the topic) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 18
118 Components of a Retrieval Model The user: Search expert (e.g., librarian) vs. non-expert Backgound of the user (knowledge of the topic) In-depth searching vs. just-wanna-get-an-idea searching Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 18
119 Components of a Retrieval Model The user: Search expert (e.g., librarian) vs. non-expert Backgound of the user (knowledge of the topic) In-depth searching vs. just-wanna-get-an-idea searching The documents: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 18
120 Components of a Retrieval Model The user: Search expert (e.g., librarian) vs. non-expert Backgound of the user (knowledge of the topic) In-depth searching vs. just-wanna-get-an-idea searching The documents: Different languages Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 18
121 Components of a Retrieval Model The user: Search expert (e.g., librarian) vs. non-expert Backgound of the user (knowledge of the topic) In-depth searching vs. just-wanna-get-an-idea searching The documents: Different languages Semi-structured (e.g. HTML or XML) vs. plain Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 18
122 Document Representation Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 19
123 Meta-descriptions Document Representation Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 19
124 Document Representation Meta-descriptions Field information (author, title, date) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 19
125 Document Representation Meta-descriptions Field information (author, title, date) Key words Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 19
126 Document Representation Meta-descriptions Field information (author, title, date) Key words - Predefined Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 19
127 Document Representation Meta-descriptions Field information (author, title, date) Key words - Predefined - Manually extracted (by author/editor) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 19
128 Document Representation Meta-descriptions Field information (author, title, date) Key words - Predefined - Manually extracted (by author/editor) Content: automatically identifying what the document is about Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 19
129 Document Representation Controlled Vocabulary Free Text Manual Automatic Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 20
130 Document Representation Controlled Vocabulary Free Text Manual Current indexing practice Automatic Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 20
131 Document Representation Manual Automatic Controlled Current indexing Text categorization Vocabulary practice intelligent IR Free Text Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 20
132 Document Representation Manual Automatic Controlled Current indexing Text categorization Vocabulary practice intelligent IR Current indexing Free Text practice Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 20
133 Document Representation Manual Automatic Controlled Current indexing Text categorization Vocabulary practice intelligent IR Current indexing Text search engines Free Text practice statistical IR Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 20
134 Controlled Vocabularies Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 21
135 Examples are: Controlled Vocabularies Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 21
136 Examples are: Controlled Vocabularies ACM Computing Classification System Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 21
137 Examples are: Controlled Vocabularies ACM Computing Classification System An article on Web search engines would (probably) be classified as H.3.5 where: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 21
138 Examples are: Controlled Vocabularies ACM Computing Classification System An article on Web search engines would (probably) be classified as H.3.5 where: - H: Information Systems Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 21
139 Examples are: Controlled Vocabularies ACM Computing Classification System An article on Web search engines would (probably) be classified as H.3.5 where: - H: Information Systems - H.3: Information Storage and Retrieval Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 21
140 Examples are: Controlled Vocabularies ACM Computing Classification System An article on Web search engines would (probably) be classified as H.3.5 where: - H: Information Systems - H.3: Information Storage and Retrieval - H.3.5: Online Information Services Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 21
141 Examples are: Controlled Vocabularies ACM Computing Classification System An article on Web search engines would (probably) be classified as H.3.5 where: - H: Information Systems - H.3: Information Storage and Retrieval - H.3.5: Online Information Services NLM Medical Subject Headings (MeSH) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 21
142 Examples are: Controlled Vocabularies ACM Computing Classification System An article on Web search engines would (probably) be classified as H.3.5 where: - H: Information Systems - H.3: Information Storage and Retrieval - H.3.5: Online Information Services NLM Medical Subject Headings (MeSH) Yahoo! Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 21
143 Manual vs. Automatic Indexing Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 22
144 Manual vs. Automatic Indexing Pros of manual indexing: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 22
145 Manual vs. Automatic Indexing Pros of manual indexing: + Human judgements are most reliable Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 22
146 Manual vs. Automatic Indexing Pros of manual indexing: + Human judgements are most reliable + Searching controlled vocabularies is more efficient Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 22
147 Manual vs. Automatic Indexing Pros of manual indexing: + Human judgements are most reliable + Searching controlled vocabularies is more efficient Cons of manual indexing: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 22
148 Manual vs. Automatic Indexing Pros of manual indexing: + Human judgements are most reliable + Searching controlled vocabularies is more efficient Cons of manual indexing: Time consuming Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 22
149 Manual vs. Automatic Indexing Pros of manual indexing: + Human judgements are most reliable + Searching controlled vocabularies is more efficient Cons of manual indexing: Time consuming The person using the retrieval system has to be familiar with the classification system Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 22
150 Manual vs. Automatic Indexing Pros of manual indexing: + Human judgements are most reliable + Searching controlled vocabularies is more efficient Cons of manual indexing: Time consuming The person using the retrieval system has to be familiar with the classification system Classification systems are sometimes incoherent Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 22
151 Automatic Content Representation Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 23
152 Automatic Content Representation Using natural language understanding? Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 23
153 Automatic Content Representation Using natural language understanding? Computationally too expensive in real-world settings Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 23
154 Automatic Content Representation Using natural language understanding? Computationally too expensive in real-world settings Coverage Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 23
155 Automatic Content Representation Using natural language understanding? Computationally too expensive in real-world settings Coverage Language dependence Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 23
156 Automatic Content Representation Using natural language understanding? Computationally too expensive in real-world settings Coverage Language dependence The resulting representations may be too explicit to deal with the vagueness of a user s information need Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 23
157 Automatic Content Representation Using natural language understanding? Computationally too expensive in real-world settings Coverage Language dependence The resulting representations may be too explicit to deal with the vagueness of a user s information need Alternative: a document is simply an unstructured set of words appearing in it: bag of words Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 23
158 Bag-of-Words Approach Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
159 Bag-of-Words Approach A document is an unordered list of words Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
160 Bag-of-Words Approach A document is an unordered list of words Grammatical information is lost Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
161 Bag-of-Words Approach A document is an unordered list of words Grammatical information is lost Tokenization: What is a word? Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
162 Bag-of-Words Approach A document is an unordered list of words Grammatical information is lost Tokenization: What is a word? Is White House one or two words? Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
163 Bag-of-Words Approach A document is an unordered list of words Grammatical information is lost Tokenization: What is a word? Is White House one or two words? Case folding Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
164 Bag-of-Words Approach A document is an unordered list of words Grammatical information is lost Tokenization: What is a word? Is White House one or two words? Case folding President Bush becomes president, bush Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
165 Bag-of-Words Approach A document is an unordered list of words Grammatical information is lost Tokenization: What is a word? Is White House one or two words? Case folding President Bush becomes president, bush Stemming or lemmatization Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
166 Bag-of-Words Approach A document is an unordered list of words Grammatical information is lost Tokenization: What is a word? Is White House one or two words? Case folding President Bush becomes president, bush Stemming or lemmatization Morphological information is thrown away Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
167 Bag-of-Words Approach A document is an unordered list of words Grammatical information is lost Tokenization: What is a word? Is White House one or two words? Case folding President Bush becomes president, bush Stemming or lemmatization Morphological information is thrown away agreements becomes agreement (lemmatization) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
168 Bag-of-Words Approach A document is an unordered list of words Grammatical information is lost Tokenization: What is a word? Is White House one or two words? Case folding President Bush becomes president, bush Stemming or lemmatization Morphological information is thrown away agreements becomes agreement (lemmatization) or even agree (stemming) Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 24
169 Example Bag of Words Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 25
170 Example Bag of Words Scientists have found compelling new evidence of possible ancient microscopic life on Mars, derived from magnetic crystals in a meteorite that fell to Earth from the red planet, NASA announced on Monday. Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 25
171 Example Bag of Words Scientists have found compelling new evidence of possible ancient microscopic life on Mars, derived from magnetic crystals in a meteorite that fell to Earth from the red planet, NASA announced on Monday. a, ancient, announced, compelling, crystals, derived, earth, evidence, fell, found, from (2 ), have, in, life, magnetic, mars, meteorite, microscopic, monday, nasa, new, of, on (2 ), planet, possible, red, scientists, that, the, to Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 25
172 What is this about? Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 26
173 What is this about?? added, al, an, and, ballots, been, completed, count, county (2 ), even, former, gore, ground, had, hand, have (2 ), he, if, in (2 ), independent, lost, many, miamidade, might, new, not, of, president, presidential, requested, shows, study, that, the, vice, votes, would Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 26
174 What is this about?? = added, al, an, and, ballots, been, completed, count, county (2 ), even, former, gore, ground, had, hand, have (2 ), he, if, in (2 ), independent, lost, many, miamidade, might, new, not, of, president, presidential, requested, shows, study, that, the, vice, votes, would An independent study shows former Vice President Al Gore would not have added many new votes in Miami-Dade County and might even have lost ground in that county, if the hand count of presidential ballots he requested had been completed. Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 26
175 Boolean Retrieval Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
176 Boolean Retrieval Boolean operators are: AND (NEAR), OR, NOT Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
177 Boolean Retrieval Boolean operators are: AND (NEAR), OR, NOT The semantics of the Boolean operators: Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
178 Boolean Retrieval Boolean operators are: AND (NEAR), OR, NOT The semantics of the Boolean operators: t 1 AND t 2 = Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
179 Boolean Retrieval Boolean operators are: AND (NEAR), OR, NOT The semantics of the Boolean operators: t 1 AND t 2 = {d t 1 r(d)} {d t 2 r(d)} Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
180 Boolean Retrieval Boolean operators are: AND (NEAR), OR, NOT The semantics of the Boolean operators: t 1 AND t 2 = {d t 1 r(d)} {d t 2 r(d)} Documents whose representation contains t 1 and t 2 Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
181 Boolean Retrieval Boolean operators are: AND (NEAR), OR, NOT The semantics of the Boolean operators: t 1 AND t 2 = {d t 1 r(d)} {d t 2 r(d)} Documents whose representation contains t 1 and t 2 t 1 OR t 2 = Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
182 Boolean Retrieval Boolean operators are: AND (NEAR), OR, NOT The semantics of the Boolean operators: t 1 AND t 2 = {d t 1 r(d)} {d t 2 r(d)} Documents whose representation contains t 1 and t 2 t 1 OR t 2 = {d t 1 r(d)} {d t 2 r(d)} Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
183 Boolean Retrieval Boolean operators are: AND (NEAR), OR, NOT The semantics of the Boolean operators: t 1 AND t 2 = {d t 1 r(d)} {d t 2 r(d)} Documents whose representation contains t 1 and t 2 t 1 OR t 2 = {d t 1 r(d)} {d t 2 r(d)} Documents whose representation contains t 1 or t 2 Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
184 Boolean Retrieval Boolean operators are: AND (NEAR), OR, NOT The semantics of the Boolean operators: t 1 AND t 2 = {d t 1 r(d)} {d t 2 r(d)} Documents whose representation contains t 1 and t 2 t 1 OR t 2 = {d t 1 r(d)} {d t 2 r(d)} Documents whose representation contains t 1 or t 2 NOT t 1 = Introduction to Information Retrieval, Spring 2002, Week 1 Copyright c Christof Monz & Maarten de Rijke 27
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