introduction
play

Introduction Information Retrieval Indian Statistical Institute - PowerPoint PPT Presentation

Jan 17, 2024 •915 likes •1.16k views

Introduction Information Retrieval Indian Statistical Institute Information Retrieval (ISI) Introduction 1 / 20 Course details Books [MRS] Introduction to Information Retrieval , Manning, Raghavan, Schtze. https://nlp.stanford.edu/IR-book/

  1. Introduction Information Retrieval Indian Statistical Institute Information Retrieval (ISI) Introduction 1 / 20

  2. Course details Books [MRS] Introduction to Information Retrieval , Manning, Raghavan, Schütze. https://nlp.stanford.edu/IR-book/ [BCC] Information Retrieval Implementing and Evaluating Search Engines , Büttcher, Clarke, Cormack. http://www.ir.uwaterloo.ca/book/ [CMS] Search Engines: Information Retrieval in Practice , Croft, Metzler, Strohman. http://www.search-engines-book.com/ Foundations and Trends in Information Retrieval (FTIR) https://www.nowpublishers.com/INR Weightage: Mid-sem 20% Project 30% End-sem 50% Slides: Available from http://www.isical.ac.in/~mandar/courses.html and http://www.isical.ac.in/~debapriyo Information Retrieval (ISI) Introduction 2 / 20

  3. Terminology Problem definition: Given a user’s information need , find documents satisfying that need. Information need: what user is looking for Query: actual representation of above Document: any unit / item that can be retrieved For this course, we will only consider textual information (no images/graphics, maps, speech, video, etc.). Information Retrieval (ISI) Introduction 3 / 20

  4. Overview INDEXING Document Index collection Results Retrieval QUERYING engine Information Retrieval (ISI) Introduction 4 / 20

  5. Steps 1. Document acquisition: how is the document collection obtained / constructed? ( LATER ) 2. Indexing: representing documents so that retrieval is easy 3. Retrieval: matching the user query against documents in the collection 4. Evaluation: how to determine whether the system did well? ( NEXT WEEK ) Information Retrieval (ISI) Introduction 5 / 20

  6. Bag of words approach Indexing: document → list of keywords / content-descriptors / terms user’s information need → (natural-language) query → list of keywords Retrieval: measure overlap between query and documents. Information Retrieval (ISI) Introduction 6 / 20

  7. Indexing 1. Tokenisation 2. Stopword removal 3. Stemming 4. Phrase identification 5. Named entity extraction Information Retrieval (ISI) Introduction 7 / 20

  8. Indexing – I Tokenisation: identify individual words. Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on full-text or other content-based indexing. ⇓ Information retrieval IR is the activity of obtaining . . . Information Retrieval (ISI) Introduction 8 / 20

  9. Indexing – II Stopword removal: eliminate common words . . . Information retrieval IR is the activity of obtaining Information Retrieval (ISI) Introduction 9 / 20

  10. Indexing – III Stemming: reduce words to a common root. e.g. resignation, resigned, resigns → resign for common languages, use standard algorithms (Porter). Information Retrieval (ISI) Introduction 10 / 20

  11. Indexing – IV Phrases: multi-word terms e.g. computer science, data mining. Syntactic/linguistic methods use a part of speech tagger look for particular POS sequences, e.g., NN NN, JJ NN Example: computer/NN science/NN Information Retrieval (ISI) Introduction 11 / 20

  12. Indexing – IV Statistical methods: f ( a,b ) > θ (threshold) Raw frequency: f raw ( a, b ) = n ( a,b ) Dice coefficient: f dice ( a, b ) = 2 × n ( a,b ) / ( n a + n b ) n a , n b number of bi-grams whose first (second) word is a ( b ) . . . Information Retrieval (ISI) Introduction 12 / 20

  13. Indexing Document collection → Term-Document Matrix Vocabulary : set of all t 1 t 2 . . . t M words in collection D 1 D 2 N × M binary . Document collection (0-1) matrix . . D N Information Retrieval (ISI) Introduction 13 / 20

  14. Retrieval models Information Retrieval (ISI) Introduction 14 / 20

  15. Boolean model Keywords combined using AND , OR , ( AND ) NOT e.g. (medicine OR treatment) AND (hypertension OR “high blood pressure”) Information Retrieval (ISI) Introduction 15 / 20

  16. Boolean model Keywords combined using AND , OR , ( AND ) NOT e.g. (medicine OR treatment) AND (hypertension OR “high blood pressure”) Efficient and easy to implement (list merging) AND ≡ intersection OR ≡ union Example: medicine → D 1 , D 4 , D 5 , D 10 , . . . hypertension → D 2 , D 4 , D 8 , D 10 , . . . Information Retrieval (ISI) Introduction 15 / 20

  17. Boolean model Keywords combined using AND , OR , ( AND ) NOT e.g. (medicine OR treatment) AND (hypertension OR “high blood pressure”) Efficient and easy to implement (list merging) AND ≡ intersection OR ≡ union Example: medicine → D 1 , D 4 , D 5 , D 10 , . . . hypertension → D 2 , D 4 , D 8 , D 10 , . . . Drawbacks OR — one match as good as many AND — one miss as bad as all no ranking queries may be difficult to formulate Information Retrieval (ISI) Introduction 15 / 20

  18. Vector space model (VSM) Any text item (“document”) is represented as list of terms and associated weights. t 1 t 2 . . . t M D 1 w 11 w 12 w 1 M D 2 w 21 w 22 w 2 M . . . D N w N 1 w N 2 w NM Term = keywords or content-descriptors Weight = measure of the importance of a term in representing the information contained in the document Information Retrieval (ISI) Introduction 16 / 20

  19. Term weights Term frequency (tf) repeated words are strongly related to content importance does not grow linearly with frequency ⇒ use sub-linear function examples: tf ( ) 1 + log( tf ) , 1 + log 1 + log( tf ) , k + tf Inverse document frequency (idf): uncommon term is more important Example: medicine vs. antibiotic commonly used functions N log N − df + 0 . 5 log 1 + df , df + 0 . 5 Information Retrieval (ISI) Introduction 17 / 20

  20. Term weights Normalisation by document length: term-weights for long documents should be reduced long docs. contain many distinct words. long docs. contain same word many times. Intuition: each term covers a smaller portion of the overall information content of a long document use # bytes, # distinct words, Euclidean length, etc. Weight = tf x idf / normalisation Information Retrieval (ISI) Introduction 18 / 20

  21. Term weights: “traditional” weighting schemes Cosine normalisation N (1 + log( tf )) × log 1+ df √∑ w 2 i Pivoted normalisation 1+log( tf ) log( N × df ) 1+log( average tf ) (1 . 0 − slope ) × pivot + slope × # unique terms Information Retrieval (ISI) Introduction 19 / 20

  22. VSM: retrieval Measure vocabulary overlap between user query and documents. t 1 . . . t M Q = q 1 . . . q M D = d 1 . . . d M Q.⃗ ⃗ Sim ( Q, D ) = D = ∑ i q i × d i more matches between Q, D ⇒ Sim ( Q, D ) ↑ matches on important terms between Q, D ⇒ Sim ( Q, D ) ↑ Information Retrieval (ISI) Introduction 20 / 20

  23. VSM: retrieval Measure vocabulary overlap between user query and documents. t 1 . . . t M Q = q 1 . . . q M D = d 1 . . . d M Q.⃗ ⃗ Sim ( Q, D ) = D = ∑ i q i × d i more matches between Q, D ⇒ Sim ( Q, D ) ↑ matches on important terms between Q, D ⇒ Sim ( Q, D ) ↑ Use inverted list (index). t i → ( D i 1 , w i 1 ) , . . . , ( D i k , w i k ) Information Retrieval (ISI) Introduction 20 / 20

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION

INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION

TRAINING COURSE ON SOCIAL PROTECTION & FORMALIZATION TRINIDAD AND TOBAGO MARCH 15, 2017 INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION Design of the NIS Assistance from the

547 views • 23 slides
INTRODUCTION Introduction 2/42 INTRODUCTION Alternations I am giving bread to the

INTRODUCTION Introduction 2/42 INTRODUCTION Alternations I am giving bread to the

Patterns of variation in the expression of case and agreement Andrs Brny Leiden University Centre for Linguistics 9 November 2019, BaSIS Workshop a.barany@hum.leidenuniv.nl INTRODUCTION Introduction 2/42 INTRODUCTION

1.37k views • 42 slides
Introduction Introduction Introduction Introduction Outline Motivation Failures

Introduction Introduction Introduction Introduction Outline Motivation Failures

Introduction Introduction Introduction Introduction Outline Motivation Failures Definition and concepts Process and product properties Principles SE Approaches Motivation Software and the economy The economies of

1.03k views • 49 slides
Introduction Introduction Introduction Nationwide Cause for Concern 1

Introduction Introduction Introduction Nationwide Cause for Concern 1

Introduction Introduction Introduction Nationwide Cause for Concern 1 https://www.cdc.gov/std/stats17/infographic.htm Introduction Epidemiology in Western Colorado CPHE Surveillance Data Report, May 2018 Chlamydia Epidemiology in Western

977 views • 39 slides
DAQ introduction Purpose of this talk : (1) Introduction for those who have not been in every

DAQ introduction Purpose of this talk : (1) Introduction for those who have not been in every

DAQ introduction Purpose of this talk : (1) Introduction for those who have not been in every meeting (2) Some ideas of diagrams for section 7.1 (Introduction) of TP. Giles Barr 25 Jan 2018, Pembroke College On DUNE, the reality is there is a

474 views • 10 slides
INTRODUCTION cf. Schneider, Chapter 1 INTRODUCTION THEY ARE OUT TO GET YOU INTRODUCTION WHAT

INTRODUCTION cf. Schneider, Chapter 1 INTRODUCTION THEY ARE OUT TO GET YOU INTRODUCTION WHAT

ADVANCED COMPUTER SECURITY INTRODUCTION cf. Schneider, Chapter 1 INTRODUCTION THEY ARE OUT TO GET YOU INTRODUCTION WHAT IS SECURITY? A systems security policies describe What the system is supposed to do Store and provide access

479 views • 16 slides
Overview Introduction to SMIL Introduction to W3C and XML Introduction to SMIL

Overview Introduction to SMIL Introduction to W3C and XML Introduction to SMIL

Overview Introduction to SMIL Introduction to W3C and XML Introduction to SMIL Writing a SMIL file Adding Media Objects Martin Halvey Clipping media files January 2008 Timing and Synchronising Layout World Wide

387 views • 14 slides
14 Introduction Introduction Bad guys can put malware into Example: hosts

14 Introduction Introduction Bad guys can put malware into Example: hosts

Introduction Introduction source Encapsulation ISO/OSI reference model message M application segment H t H t M transport network datagram H n H n H t M presentation: allow applications to frame link H l H n H t M interpret

583 views • 4 slides
Previous Lecture Introduction to SMIL 2 Introduction to W3C and XML Introduction to SMIL

Previous Lecture Introduction to SMIL 2 Introduction to W3C and XML Introduction to SMIL

Previous Lecture Introduction to SMIL 2 Introduction to W3C and XML Introduction to SMIL Writing a SMIL file Adding Media Objects Martin Halvey Clipping media files January 2008 Timing and Synchronising Layout

322 views • 8 slides
Lecture 1 Andreas Habegger Introduction Zynq Introduction Zynq Introduction Zynq PS vs. PL

Lecture 1 Andreas Habegger Introduction Zynq Introduction Zynq Introduction Zynq PS vs. PL

Introduction Zynq Lecture 1 Andreas Habegger Introduction Zynq Introduction Zynq Introduction Zynq PS vs. PL Processing System Processor Peripherals Data Buses AXI Bus Conclusion BTE5380 - Embedded Systems Octobre 2014 Andreas Habegger

1.81k views • 30 slides
2018.06 01 SMILE5 Introduction S E 5 02 Alpha Cloud M I L 03 Company Introduction 04

2018.06 01 SMILE5 Introduction S E 5 02 Alpha Cloud M I L 03 Company Introduction 04

2018.06 01 SMILE5 Introduction S E 5 02 Alpha Cloud M I L 03 Company Introduction 04 Project References S E 5 PART.1 SMILE5 Introduction M I L Introduction STORION-SMILE5 Residential Energy Storage System Inverter 5kW Output Input

1.06k views • 25 slides
Introduction to CICS Course introduction Course introduction What is CICS? What is an

Introduction to CICS Course introduction Course introduction What is CICS? What is an

Introduction to CICS Course introduction Course introduction What is CICS? What is an application server? Why use an application server? Course introduction Services provided by CICS How CICS applications are defined Scaling CICS to meet

1.84k views • 146 slides
Network Visualization Introduction Presented by Shahed Introduction Introduction Basic

Network Visualization Introduction Presented by Shahed Introduction Introduction Basic

Network Visualization Introduction Presented by Shahed Introduction Introduction Basic building blocks Node Links (relationship between nodes) Spatial information Network data

584 views • 9 slides
Introduction Introduction (1) Main argument of the paper: universities are not only

Introduction Introduction (1) Main argument of the paper: universities are not only

Le jugement des pairs comme outil de management des universits Peer-review as a management tool Christine Musselin (CSO, Sciences Po et CNRS) Avril 2013, Lirrsistible ascension du capitalisme acadmique Introduction Introduction (1)

590 views • 28 slides
Introduction Introduction to storage and to storage and filesystems filesystems Introduction

Introduction Introduction to storage and to storage and filesystems filesystems Introduction

Moreno Baricevic Gilberto Daz Axel Kohlmeyer Stefano Cozzini ULA ICTP CNR-IOM DEMOCRITOS Merida, VENEZUELA Trieste, ITALY Trieste, ITALY Introduction Introduction to storage and to storage and filesystems filesystems Introduction

1.19k views • 51 slides
JABLOTRON JA-100 introduction November 2011 Introduction Todays goals First introduction of

JABLOTRON JA-100 introduction November 2011 Introduction Todays goals First introduction of

JABLOTRON JA-100 introduction November 2011 Introduction Todays goals First introduction of the brand new JA-100 alarm system Visions Basic architecture System components Setting Help us with final validations You are the FIRST ones

1.69k views • 111 slides
Decision Procedures An Algorithmic Point of View Revision 1.0 D.Kroening O.Strichman Outline 1

Decision Procedures An Algorithmic Point of View Revision 1.0 D.Kroening O.Strichman Outline 1

Arrays Chapter 7 Decision Procedures An Algorithmic Point of View Revision 1.0 D.Kroening O.Strichman Outline 1 Introduction Definition Basic Operations Syntax Semantics Example 2 Arrays as Uninterpreted Functions 3 A Reduction Algorithm

760 views • 38 slides
CS 225 Data Structures Oc October 31 He Heaps and Priority Qu Queues G G Carl Evans Ru

CS 225 Data Structures Oc October 31 He Heaps and Priority Qu Queues G G Carl Evans Ru

CS 225 Data Structures Oc October 31 He Heaps and Priority Qu Queues G G Carl Evans Ru Running T Times Hash Table AVL Linked List SUHA: Find Worst Case: SUHA: Insert Worst Case: Storage Space Se Secr cret, M Mystery D

429 views • 27 slides
Properties of the automorphism group and a probabilistic construction of a class of countable

Properties of the automorphism group and a probabilistic construction of a class of countable

Properties of the automorphism group and a probabilistic construction of a class of countable labeled structures Dragan Ma sulovi c and Igor Dolinka Department of Mathematics and Informatics University of Novi Sad, Serbia AAA 83 Novi

479 views • 24 slides
Polynomial completeness properties Erhard Aichinger Department of Algebra Johannes Kepler

Polynomial completeness properties Erhard Aichinger Department of Algebra Johannes Kepler

Polynomial completeness properties Erhard Aichinger Department of Algebra Johannes Kepler University Linz, Austria June 2012, AAA84 Polynomials Definition A = A , F an algebra, n N . Pol k ( A ) is the subalgebra of A A k = { f

604 views • 59 slides
Number Theory and Algebra: A Brief Introduction Rana Barua Indian Statistical Institute Kolkata

Number Theory and Algebra: A Brief Introduction Rana Barua Indian Statistical Institute Kolkata

Number Theory and Algebra: A Brief Introduction Rana Barua Indian Statistical Institute Kolkata May 15, 2017 university-logo-isi Rana Barua Number Theory and Algebra: A Brief Introduction Elementary Number Theory: Modular Arithmetic

688 views • 14 slides
Sujoy Das & Aarti Kumar Associate Professor Research Scholar Department of

Sujoy Das & Aarti Kumar Associate Professor Research Scholar Department of

Performance Evaluation of Dictionary Based CLIR Strategies for Cross Language News Story Search Presented by: Sujoy Das & Aarti Kumar Associate Professor Research Scholar Department of Computer Applications MANIT, Bhopal

987 views • 22 slides
Improving IR performance from OCRed text using cooccurrence RISOT 2012 Kripabandhu Ghosh and

Improving IR performance from OCRed text using cooccurrence RISOT 2012 Kripabandhu Ghosh and

Improving IR performance from OCRed text using cooccurrence RISOT 2012 Kripabandhu Ghosh and Anirban Chakraborty Indian Statistical Institute Kolkata, India Kripabandhu GhoshandAnirban Chakraborty Indian Statistical Institute Improving IR

697 views • 22 slides
Quantitative estimates of a drainage network model Rahul Roy Indian Statistical Institute, New

Quantitative estimates of a drainage network model Rahul Roy Indian Statistical Institute, New

Quantitative estimates of a drainage network model Rahul Roy Indian Statistical Institute, New Delhi joint work with Kumarjit Saha and Anish Sarkar. Hurst exponent A river basin network is an anisotropic system defined by a longitudinal length

453 views • 42 slides

More recommend