ch1 intro to information retrieval-lina nemri

8/2/2019 Ch1 intro to information retrieval-Lina Nemri

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Information Retrieval

Lebanese UniversityFaculty of Economics and Business

Administration 1st Branch

Class: M1

Instructor: Dr. Lina A. Nimri

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Introduction

Modern Information Retrieval, Chapter 1

Ricardo Baeza-Yates, Berthier Ribeiro-Neto
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Introduction Examples of information need in the context of the

world wide web:

Find all documents containing information oncomputer courses which:

(1) are offered by universities in South England, and(2) are accredited by the BCS/IEE bodies,

To be relevant, the document must include information on admissionrequirements, and e-mail and phone number for contact purpose.

Find all docs containing information on college tennisteams which:

(1) are maintained by a USA university and

(2) participate in the NCAA tournament.

Information Retrieval 4
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5

Information Retrieval

Retrieval System

Query

Set of retrieved documents

Documents

User Information Need

Search Engine

Useful or relevantinformation to the user

Primary goal of an IR system

Retrieve all the documents which are relevant to a user query,

while retrieving as few non-relevant documents as possible.

Representation, storage, organisation, and access to

information items

(Usually) keyword-based representation
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Data Retrieval

Determine which documents contain thekeywords in the user query is not always enough

to satisfy the user information need. Data Retrieval retrieves objects which satisfy

clearly defined conditions, such as regularexpressions or relational algebra expressions.

Data Retrieval system deals with data with well-defined structure and semantics

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Information Retrieval System

Retrieving information about a subject

Deals with natural language text which is

not well structured and could besemantically ambiguous

It must interpret the contents of

documents and rank them according tothe degree of relevance to the user need.

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Area of interest

Digital Libraries

Information experts

World Wide Web - Very difficult task The hyperspace is vast

The absence of a well defined data model

(format or representation form)

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Effective retrieval

The effective retrieval of relevantinformation is directly affected by: The user task

The logical view of the document(documents representation) adopted by

the retrieval system.

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Pulling

The user can browse the documents when hismain objectives are not clear in the beginningand whose purpose might change during the

interaction with the system. Combination of retrieval and browsing is not yet

a well established approach.

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Retrieval

Browsing

Database
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Documents

Unit of retrieval A passage of free text

composed of text, strings of characters

from an alphabet composed of natural language

newspaper article, a journal paper, adictionary definition, email messages

size of documents arbitrary

newspaper article vs. journal paper vs. email

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What is a document?

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Representation of documents Documents are represented thru a set of index terms or

keywords or term descriptors extracted directly form text specified by human subjects (information science) metadata

Most concise representation Poor quality of retrieval

Full text representation Most complete representation High computational cost

Large collections

Reduce set of representative keywords Elimination of stop words Stemming Identification of noun phrases Further compression and indexing

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Document term

descriptors toaccess texts

Generation ofdescriptors fortext

By hand

By analysing the text
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Logical View of the documents

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structure

Accents

spacing stopwordsNoun

groups stemmingManual

indexingDocs

structure Full text Index terms
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The retrieval functions

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Information need

Query

Formulation

Documents

Document representation

Indexing

Retrieved documents

Retrievalfunctions

Relev

ance

feedb

ack
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Queries

Information Need: Simple queries

composed of two or three, perhaps even

dozens, of keywords e.g., as in web retrieval

Boolean queries

neural networks AND speech recognition Context Queries

Proximity search, phrase queries

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User termdescriptorscharacterisingthe user need
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Best-Match retrieval

Compare the terms in a documentand query

Compute similarity between eachdocument in the collection and thequery based on the terms that theyhave in common

Sorting the documents in order ofdecreasing similarity with the query

The outputs are a ranked list and

displayed to the user - the top onesare more relevant as judged by thesystem

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Document termdescriptors toaccess texts

User termdescriptorscharacterisingthe user need
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Conceptual view of text

retrieval system

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QueriesDocuments

Similarity

Computation

RetrievedDocuments
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Expanded view of text

retrieval system

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Queries DocumentsIndexing

Indexed

Documents

Similarity

Computation

RetrievedDocuments

RankedDocuments
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Process of retrieving info

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User Interface

Text Operations

QueryOperations

Indexing

Similarity Computation(Searching)

Ranking

DocumentRepositoryManager

Index

Userneed

Logical view Logical view

Inverted

file

Query

Retrieved docs

Text

TextUser feedback

Ranked docs

Textrepository
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Key Topics

Indexing text documents

Retrieving text documents

Evaluation Query reformulations

Search Engines=

IR + Link Structure + Name Interpretation

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Information Retrievalvs Information Extraction

Information Retrieval Given a set of query terms and a set of document

terms select only the most relevant documents[precision], and preferably all the relevant [recall].

Information Extraction Extract from the text what the document means.

IR systems can FIND documents but need notunderstand them

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