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› Gertjan van Noord – based on the sheets by Leonoor van der Beek 2013

Information Retrieval

Lecture 1: introduction

Agenda for today

• Who’s who• Intro to the course• Chapter 1 of Introduction to Information

Retrieval• Homework/lab assignment

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Intro to the course

• What is IR?• What will we study and how?• Objectives of the course• Exercises and lab sessions• Final exam

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What is IR?

Individuals, administrations, organizations have lots of digital information

• how to organize and store it?• how to retrieve documents?• how to retrieve info inside them?

An IR system is a tool to facilitate retrieval of such information

Book’s definition

Information retrieval (IR) isfinding material (usually documents)of an unstructured nature (usually text)that satisfies an information needfrom within large collections(usually stored on computers).

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... finding material (usually documents) ...

What else can you think of?

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• parts of documents• facts, like the day of birth of Rembrandt• a book in the library• a work of art in a museum

... from within large collections (usually stored on computers)…

WWW? What else?

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• Specific collections, like legal information or scientific medical papers (Medline)

• Information on your own computer• Information within a company• Subparts of the www, like one domain

… of an unstructured nature (usually text)

Can you explain this?

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Unstructured: differences between text and databasesis a text document really unstructured?how about XML?Beyond text: image, sound, video, ….

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Database search vs. IR •structured semantic info:

• fields• datatypes• validation• relations

•search of fields•exact search for data

•order of found records alfanumerical

•no semantic structureno fixed format, but

• text structure• metadata• XML

•full text search•not-exact search for data or information•order of found documents often by similarity with query

Book’s definition

Information retrieval (IR) isfinding material (usually documents)of an unstructured nature (usually text)that satisfies an information needfrom within large collections(usually stored on computers).

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that satisfies an information need ...

What information needs can we discern?Try to formulate some different types of

goals of a search

-facts and question answering-definitions-information on a subject-retrieving a known document

and in websearch?

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User needs in web search

Navigational. The immediate intent is to reach a particular site.

Informational. The intent is to acquire some information assumed to be present on one or more web pages.

Transactional. The intent is to perform some web-mediated activity.

Broder, A. 2002 A taxonomy of Web search. SIGIR Forum 36, no.23-10

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Translation of info need

Each information need has to be translated into the "language" of the IR system

reality document

info need query

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Translation of info need

Query: Hilton, Paris

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Translation of info need

Query: champagne

Translation of info need

Query: Rene Froger “Een eigen huis”

Translation of info need

Information need:

Query: ??

Translation of info need

Information need:

Query: ??

Are the results satisfying?

Search engines produce often a lot of resultsWhen are you satisfied with the results?How can we evaluate a system?

• the most relevant results are easy to find (on top of the list, and/or sorted by subject, …)

• only few results are not relevant• new information• info corrobarated (more sources)• relevant documents that I know are presented

Precision and recall

Key statistics for evaluation with a test set (fixed questions, set of documents, evaluations of documents for the queries available)

Precision: what fraction of the results are relevant to the information need?

Recall: what fraction of the relevant documents in the collection were returned by the system?

Precision and recall

But how relevant are Precision and Recall if you search for e.g. The date of birth of Vincent van Gogh?

Overview of an IR system

(book: Baeza-Yates:Modern IR)

Web site

Overview and exercises:

* http://www.let.rug.nl/vannoord/College/Zoekmachines/

* Nestor

Course Book

Introduction to Information RetrievalD. Manning, P. Raghaven and H. Schütze

Online version

NB: the book is also used for the Information Retrieval course

The book is written for CS students, we will skip sections and exercises that are a bit too technical

Schedule for this coursewk 1ch 1 boolean retrieval, posting lists wk 2ch 2 decoding, tokenization and

normalization, sublinear posting list intersection

wk 3ch 3 dictionaries, wild cards, spell correction

wk 4ch 6 scoring and term weighting, term and document frequency weighting, vector space models

wk 5ch 8 evalutationwk 6ch 21 link analysis, page rankwk 7ch 9 relevance feedback and query analysis

HOW will we study the book?

Homework: read the chapter thoroughlyLectures: overview of chapter Labtime/homework: do exercises Next lecture: remaining questions

Full slide presentation of the chapters by one of the authors available as well author's slides

Labtime

1. Exercises (from the book + more)

2. Try out simple techniques in Python

3. More...

4. More...

Course objectives

• knowledge of IR terminology• insight in IR models and IR processes• knowledge of methods of indexing, querying,

retrieving and ranking• knowledge of methods of evaluation of IR

systems• practical experience with use, adaptation and

testing of some of the basic IR algorithms and techniques

Chapter 1: Boolean retrieval

1. General introduction on IR2. Boolean systems3. Representation of information4. Retrieving documents5. Efficiency aspects

Boolean retrieval

The first IR systems were Boolean systemsQueries are formulated with the Boolean

operators AND, OR and NOT:• Brutus AND Caesar • (Brutus OR Caesar) AND NOT Cleopatra• Brutus OR (Caesar AND NOT Cleopatra)• NOT Brutus

How about Google queries?

Information from documents

• Each document in the system needs a unique docID

• Tokenization is the process of splitting a text into separate tokens (not trivial!)

• For a simple boolean system we just need to know which terms are present in which doc

Term document incidence matrix

Doc 1 Doc 2 Doc 3 Doc 4

Antony 1 1 0 0

Brutus 1 1 1 0

Caesar 1 1 0 1

Cleopatra 1 0 0 0Antony AND Brutus AND NOT Cleopatra?in huge collections > 99% of entries are 0not a good representation, no efficient processing

Building an inverted file

1. Give DocIDs and tokenize the texts2. Gather terms with their docID3. Sort on terms and docID4. Now list the unique terms with their

document frequency and link to the postings list with docIDs

term docfreq postings list

[Caesar, 3] [1,2,4]

Inverted file / index

Antony AND Brutus AND NOT Cleopatra?efficient processing if sorted on DocIDsimple merging algorithms for AND / OR

(term) (df) (postings list)

Antony 2 1 , 2 , 6

Brutus 3 1 , 2 , 3

Caesar 3 1 , 2 , 4 , 5 , 6

Cleopatra 1 1

Distributive laws

a AND (b OR c) = (a AND b) OR (a AND c)(a OR b) AND (c OR d) = ??

NOT(a OR b) = NOT(a) AND NOT(b) NOT(a AND b) = ??

Conjunctive and disjunctive queries

The outer level of processing can be either conjunctive (AND) or disjunctive (OR):

• Conjunctive normal form:• a conjunction of disjunctions• (a OR NOT b) AND (c OR d) AND e

• Disjunctive normal form:• a disjunction of conjunctions• (a AND NOT b) OR (c AND d) OR e

The order of the sizeExample

f(x) = 2x3 + 5x2 +x + 9 This is a function of O(x3): if x grows to infinity the factor x3 is what really determines the size of the outcome, the rest can be neglected

The order of time complexity

ExampleTo find similar elements in two ordered lists, the

number of steps depends on the size of both lists:O(x + y) (linear)

Need to check all combinationsO(x * y) (quadratic)

Big O notation

• Used to classify algorithms by how they respond (e.g. in their processing time or working space requirements) to changes in input

• Best case, worst case, average case?• Big O represents the upper bound (worst

case)• Other symbols used for lower bound,

tight bound, ….

Guidance/questions on the text

Write down and try to find explanations of terms you don’t know

p4 you know the KB, MB, GB .. etc sizes?p5 fig 1.3: look back to fig 1.1p7 what types of linguistic preprocessing do you

see in the examples in step 3?p11/12 do you understand the algorithms?

Are you able to explain now what an inverted file is and how it is constructed?

Homework

…. is on the web site ….