Introduction to irs notes easy way learning

FOUNDATIONS OF
INFORMATION RETRIEVAL
Lynda Tamine-Lechani
lynda.lechani@irit.fr
https://www.irit.fr/~Lynda.Tamine-Lechani/

FOUNDATIONS OF INFORMATION RETRIEVAL
2
•Course description
Study the theory, design, and implementation of information retrieval systems
from the perspectives of:
ü information representation: focus on texts
ü theoretical information retrieval model: focus on language model and learning-
based models
ü Performance evaluation: focus on system-centred evaluation
•Learning objectives
ü Index and represent textual information;
ü Recall and discuss well-known information retrieval models;
ü Design, implement and evaluate the performance of information retrieval
systems using retrieval algorithms and models discussed in class.
© L. Tamine-Lechani

3
•Organization
o 12H course, 6H tutorial: Lynda Tamine-Lechani
o 10H hands-on work: Jesus-Lovon Melgajero, José G. Moréno and Lynda Tamine-
Lechani
•Prerequisites
o Python programming
o Basics in probability and statistics
•Course material
o Copies of the lecture slides are posted on the MOODLE site
o Book and readings references are provided
•Grading
o 1st session
üHands-on experience with techniques discussed in class: assignment of 30% of the final score
üFinal written exam in class: assignment of 70% of the final score
o 2nd session
üFinal written exam in class: assignment of 100% of the final score

4
•Schedule
Lecture Topic
1 Course Introduction; Text indexing, vector semantics
2 Static embeddings, contextual embeddings
3 Infomation retrieval (IR) models: query reformulation, learning to
rank
4 Tutorial 1: Text indexing and representation
5 Neural models for IR
6 Page Rank, Performance evaluation
7 Tutorial 2: information retrieval techniques and models
8 Question answering systems and chatbots
9 Tutorial 3: performance evaluation

Information retrieval: Algorithms and Heuristics
David A. Grossamnn, Ophir Frieder, Kluwer
Academic Publishers, 1998
Modern information retrieval
R.B Yates, R. Neto, ACM Press Addisson Wesley, 1999
Recherche d'information, applications, modèles et algorithmes
M.R Amini et E. Gaussier, Eyrolles 2012
Search engines in practice
B. Croft, D. Metzler, T. Trohman, Pearson 2010
Books
5

Calvin Mooers 1951 :
Information retrieval (IR) is the name for the process or method whereby a prospective user of
information is able to convert his need for information into an actual list of citations to documents in
storage containing information useful to him. .. Information retrieval is crucial to documentation and
organization of knowledge". (Mooers, 1951, p. 25)
Salton, 1980 :
Information retrieval systems are designed to help analyze and describe the items stored in a file, to
organize them and search among them, and finally to retrieve them in response to a user's query.
Designing and using a retrieval system involves four major activities: information analysis, information
organization and search, query formulation, and information retrieval and dissemination.
Information retrieval (IR) in computing and information science is the process of obtaining
information system resources that are relevant to an information need from a
collection of those resources. Searches can be based on full-text or other content-based
indexing.
6
Information Retrieval (IR): definitions
Introduction

...Yes, but also refer to:
- Search in digital libraries
- Search in campany corpus
- Search in specialized corpus (health, legal, biological –related resources)
- Search for a location
- Search for answers
- Recommend items
- Summarize reviews
- ...
7
Definitions refer to ....well-known search engines ?
Introduction

• Wide-variety of search systems, interaction environments
o Web search engines
o Conversational agents
o E commmerce: Amazon, AirBnb, ...
o Media recommendation: Netflix, Spotify, ...
8
From search to
conversation
Search and navigate on
maps
Cross-device search
Heatmaps on SERP
...with voice only!
...and different forms of user-system interactions
Introduction

(Web) search systems that select from a corpus of texts documents those that are
relevant to a user information need experssed by the user using a query.
9
Focus in this lecture
Introduction
Query Documents
Selection
Information
need
Corpus
System's
answer to the query

10
Basic notions: Document
Introduction
• Document: information unit being searched
- Document
- Paragraph
- Phrase
- Structure unit (section, chapter,...)
•Different views 1. Introduction
Information
retrieval....
2. Basics
The notion of
query…
Date : 15/01/2013
Author : Albert
Langue : Français
….
This course introdues
the basics of
information retrieval
Content
Metadata
Structure

• Different media
Text (monomedia)
Image
Multimedia
Video
11
Introduction

•Different forms
-Document
-Blog
-Tweet
-News
-Presentation
-E-mail
--..
12
Introduction

• What the user seeks for: an information need
• How the user expresses his information need : a query
In this course: a query is a
list of keywords
13
Basic notions: information need, query
Introduction

• A key concept in information retrieval
A document is relevant if it matches the information need. Numerous types of
relevance:
o Topical (aboutness) relevance: the document covers the query topic
o Situational relevance: the document matches the user's situation (e.g., task,
location, ...)
o Cognitive relevance : the documents fits with the user's knowledge state
o ...
and numerous criteria of relevance:
- Novelty
- Fresheness
- Language
- Specificity
- Trust
- ...
The main focus in this course is topical relevance: useful and "easy" to define and to
measure, but it does not cover everything related to relevance
14
Basic notions: Relevance
Introduction

15
What makes information retrieval challenging ?
Introduction
© NIST (TREC)

16
Introduction
•Deluge of information
o Large-scale information
o Often little ratio of information is relevant and/or useful for a query
o Information is noisy
o Information is not always trusty
o Hetrogeneous information forms and sources
o ...

Source : Infographic
Increasing volumes of information
available on increasing information sources: social applications
mobile devices, sensors, ...
2003
Réseaux sociaux
2001
Wiki
1998
Recherche
1995
Annuaire
1994
E-commerce
1990
WWW
1972
ARPANET
1999
Blogs
2001
Wiki
2003
Réseaux sociaux
17
Information is every where
Introduction

18
Focus on Web 3.0: The digital world today
Introduction
© L.Tamine-Lechani
•1st place: platforms for
publication/sharing of texts (mostly),
newsletters, podcasts, videos, photos,
o Wikipedia, Blogger, Google Poadcast,
youtube, Flickr, TripAdvisor, ...
•2nd place: platforms for messaging
o Facebook, Messenger, telegram,...
•3rd place: platforms for conversations
o Quora, StackExchange, Reddit, Facebook
groups, Google Groups, ...
•4th place: platforms for collaboration
o Facebook workplace, TeamWork, Chatter,
...
image credit https://fredcavazza.net/2021/05/06/panorama-des-
medias-sociaux-2021/

19
2003
Réseaux sociaux
2003
Réseaux sociaux
Source :
https://datastudio.google.com/embed/reporting/1sImC_rjeWqNXdgQt5MtmrQMbH44qFjtA/page/1fzh
• Google processes in 2020 more than 7
milliards of queries every day among
which 15% have never been submitted
before (new queries)
• The number of users in the world is
estimated as 2.77 milliards on social
media, 2.46 milliards in 2017
• 51%, or more than 240 milliards of
dollars, de tout l'argent publicitaire
dépensé dans le monde en 2019 seront
basés sur les médias numériques.
• Les ventes en ligne devraient atteindre
3.45 billions de dollars de ventes en 2020
• 47.3% de la population mondiale devrait
acheter en ligne en 2020.
Statitistics on usage
of information
access systems
2014-2020
Some statistics 2020-2021: information and users
Introduction
image credit https://www.internetlivestats.com/
• Users and information
shared in live 2021

20
What makes information retrieval challenging?
Introduction
•Information needs are ambiguous
oQueries are generally short, ambiguous
oThe matching between queries and intents is M-N
Roi lion
1 Queryà N intents
- Master UPS Intelligence artificielle
- Université paul Sabatier IA
- Formation IA Toulouse
- Matsre IAFA
..
M Queriesà 1 intent

21
Introduction
•Relevance is subjective
o Relevance is subjective
ü User-dependent
ü Situation-dependant
ü Topicality is often the threshold relevance
•Relevance faces vocabulary mismatch between queries and
documents
o Matching as word overlap: is it really semantic overlap?
Q: "most jurisdictions exercise a high degree of regulation over banks" [financial institution]
D1: "I have been stolen when I withdrew the money from the bank" [Building]
D2: "fish lined the bank of the stream" [The land alongside or sloping down to a river or lake]
o Matching is not exact, rough matching between queries and documents
Q: "Presidential Elections in France"
D1 : "Election campaign is running"
[relevant, but missing ‘presidential’ and ‘France’]
D2 : "Macron, the President of France is attending COP21"
[irrelevant, and matching ‘France’ and ‘President’]

22
Introduction
•Queries and documents vary in length
oModels must handle variable length input
oRelevant documents have irrelevant content
Q: "variant Omicron symptomes"
D: "Le variant Omicron a déjà atteint plusieurs patients en France après avoir fait son apparition en Afrique du Sud. S'il semble plus
transmissible, il ne serait pas plus virulent. Mais quels sont ses symptômes ?
Le 26 novembre dernier, l’Organisation mondiale de la Santé (OMS) qualifiait le variant Omicron, nouvellement apparu en Afrique
du Sud, de « préoccupant » sur la base de sa rapidité de propagation. De nombreux cas commencent depuis à émerger à travers le
monde, dont quelques-uns en France.
Mais concernant sa dangerosité ou ses symptômes, le grand flou règne. Alors, que savons-nous ?
En se basant sur les situations en Afrique du Sud et au Royaume-Unis, l'OMS a indiqué dans une mise au point technique que le
variant Omicron semble se propager plus vite que Delta.
Néanmoins, contrairement à ce dernier, les symptômes seraient moins sévères.
Pas de perte de goût ou d’odorat
Interrogée par la BBC, le Dr Angelique Coetzee, présidente de l’Association médicale sud-africaine, qui fut l’une des premières à
être confrontée à Omicron, a indiqué que les symptômes qu’elle a pu observer semblent moins spécifiques que ceux de la maladie
originelle. « Cela a débuté avec un patient de sexe masculin âgé d’environ 33 ans », a-t-elle expliqué lors de cet entretien.
« Il a déclaré qu’il était extrêmement fatigué ces derniers jours et se plaignait de courbatures et de légers maux de tête. » Mais
l’homme n’a pas perdu son sens du goût ni celui de l’odorat ; il avait la « gorge qui le grattait », et non pas un mal de gorge et
une toux comme avec les variants précédents.
Elle a également déclaré que les autres patients auscultés le même jour « présentaient les mêmes symptômes bénins ".
Source: https://www.leprogres.fr/magazine-sante/2021/12/13/variant-omicron-quels-sont-les-premiers-symptomes-
detectes

23
What makes information retrieval similar vs. different from data retrieval (Databases)?
Introduction
Information retrieval Data retrieval
Information unit Information Data (attribute-value)
Query Vague expression of an
information need
Vague expressio
Language of the query Natural language Formel language
Matching query-information Approximatif Exact
Selected information Information relevant to the
query
All the data that satifies the
query

Documents
Documents
representations
Information need
Query
Selected documents
Indexing Expression
Matching
Feedback
24
Copyright L.Tamine-Lechani
The basic process of information retrieval
Introduction

25
• Lecture structure
oIntroduction
o Chapter 1: Text indexing and representation
"How to transform raw texts into machinable representations?
Keywords: indexation, words, documents, representation learning of texts
o Chapter 2: Information retrieval (IR) models
"How to score the relevance of a document as an answer to a user's
query?"
Keywords: relevance status value, retrieval model
o Chapter 3: Performance evaluation of an IR system
"How to measure the performance of an information retrieval system?"
Keywords: evaluation metrics, test collections
o Chapter 4: From question-answering systems to chatbots
"How to interact with systems while searching for information?"
Keywords: conversation, turn, clarification

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