Information Retrieval Matthias Hagen
Contents I. Introduction
Objectives
Related Fields 1. Statistics
Literature Information Retrieval:
Literature Information Retrieval:
Literature “Core” information retrieval conferences:
Software Industry (Open Source):
Software Research:
Software Research:
Chapter IR:I I. Introduction
Information Retrieval in a Nutshell ❑
Information Retrieval in a Nutshell ❑
Chapter IR:I I. Introduction
Examples of Information Retrieval Problems Learn everything there is to learn about information retrieval.
Examples of Information Retrieval Problems Learn everything there is to learn about information retrieval.
Examples of Information Retrieval Problems Learn everything there is to learn about information retrieval.
Remarks: ❑
Examples of Information Retrieval Problems Plan a trip from San Francisco to Paris, France.
Examples of Information Retrieval Problems Plan a trip from San Francisco to Paris, France.
Remarks: ❑
Examples of Information Retrieval Problems What were the news today?
Examples of Information Retrieval Problems What were the news today?
Examples of Information Retrieval Problems What were the news today?
Remarks: ❑
Examples of Information Retrieval Problems Answer “Can Kangaroos jump higher than the Empire State Building?”
Examples of Information Retrieval Problems Answer “Can Kangaroos jump higher than the Empire State Building?”
Examples of Information Retrieval Problems Answer “Can Kangaroos jump higher than the Empire State Building?”
Examples of Information Retrieval Problems Answer “Can Kangaroos jump higher than the Empire State Building?”
Remarks: ❑
Examples of Information Retrieval Problems Build a fence.
Examples of Information Retrieval Problems Build a fence.
Remarks: ❑
Examples of Information Retrieval Problems Write an essay about video surveillance.
Examples of Information Retrieval Problems Write an essay about video surveillance.
Remarks: ❑
Examples of Information Retrieval Problems Given an example image, find more like it.
Examples of Information Retrieval Problems Given an example image, find more like it.
Examples of Information Retrieval Problems Given an example text, find more like it.
Examples of Information Retrieval Problems Given an example text, find more like it.
Examples of Information Retrieval Problems Given an example text, find more like it.
Examples of Information Retrieval Problems Given an example text, find more like it.
Remarks: ❑
Examples of Information Retrieval Problems Find out what people commonly write in the phrase how to ? this.
Examples of Information Retrieval Problems Find out what people commonly write in the phrase how to ? this.
Remarks: ❑
Chapter IR:I I. Introduction
Terminology Information science distinguishes the concepts data, information, and knowledge.
Remarks: ❑
Remarks: (continued) ❑
Terminology Definition 4 (Information System)
Remarks: ❑
Terminology
Terminology
Terminology
Terminology
Terminology
Terminology
Terminology
Terminology
Terminology
Terminology
Terminology Definition 7 (Information Retrieval, IR)
Terminology Definition 7 (Information Retrieval, IR)
Terminology Definition 7 (Information Retrieval, IR)
Remarks: ❑
Chapter IR:I I. Introduction
Delineation Databases, Data Retrieval
Remarks: ❑
Delineation Semiotics
Delineation Semiotics
Delineation Semiotics
Delineation Semiotics
Delineation Semiotics
Remarks: ❑
Delineation Machine Learning, Data Mining
Delineation Machine Learning, Data Mining
Delineation Machine Learning, Data Mining
Delineation Retrieval
Chapter IR:I I. Introduction
Historical Background Manual Retrieval
Remarks: ❑
Historical Background Manual Retrieval
Historical Background Manual Retrieval
Remarks: ❑
Historical Background Mechanical Retrieval
Historical Background Mechanical Retrieval
Historical Background Computerized Retrieval
Historical Background Computerized Retrieval
Remarks: ❑
Historical Background Information Retrieval (1950s)
Remarks: ❑
Historical Background Information Retrieval (1960s)
Remarks: ❑
Historical Background Information Retrieval (1970s)
Historical Background Information Retrieval (1980s – mid-1990s)
Historical Background Information Retrieval (mid-1990s – 2000s)
Historical Background Information Retrieval (today)
Historical Background Information Retrieval (today)
Historical Background Information Retrieval (today)
Historical Background Information Retrieval (today)
Chapter IR:II II. Architecture of a Search Engine
Remarks:
IR:II-3
?
Indexing Process
Acquisition conversion to
Acquisition In the acquisition step, documents are collected, prepared, and stored.
Acquisition Crawler
Acquisition Crawler
Acquisition Converter
Acquisition Document Store
Acquisition Document Store
Acquisition conversion to
Text analysis
Text Analysis The text analysis extracts from a document the “keys” by which it can be looked up
Text Analysis The text analysis extracts from a document the “keys” by which it can be looked up
Text Analysis Segmenter
Text Analysis Segmenter
Text Analysis Stopping
Text Analysis Stemming \u002f Lemmatization
Text Analysis Stemming \u002f Lemmatization
Text Analysis Link Extraction
Text Analysis Information Extraction
Text Analysis Classification
Text analysis
Text analysis
Indexing The indexing step creates the index data structures required for fast retrieval from
Indexing Term Weighting
Indexing Index Construction
Indexing Distribution
Indexing Distribution
Indexing Document Statistics
Text analysis
Chapter IR:II II. Architecture of a Search Engine
Text analysis
Text analysis
Text analysis
User Interface The user interface of a search engine allows the user to interact with the system.
User Interface Example
User Interface Example
User Interface Example
User Interface Example
Remarks: ❑
User Interface Query Language
User Interface Query Language
User Interface Result Presentation
Text analysis
Text analysis
Text analysis
User Interface Query analysis (query understanding) maps the keywords of a query to the index
Query Analysis and Synthesis Text Analysis
Query Analysis and Synthesis Logging
Remarks: ❑
Query Analysis and Synthesis Query Rewriting
Query Analysis and Synthesis Query Rewriting
Query Analysis and Synthesis Query Expansion
Remarks: ❑
Query Analysis and Synthesis Query Expansion: Relevance Feedback
Query Analysis and Synthesis Query Expansion: Relevance Feedback
Text analysis
Text analysis
Retrieval Given a query representation, the ranking step scores and orders the documents
Retrieval Document Scoring
Retrieval Document Scoring
Retrieval Document Scoring
Retrieval Document Scoring
Retrieval Distribution
Chapter IR:II II. Architecture of a Search Engine
Text analysis
Text analysis
Evaluation Overview
Remarks: ❑
Architecture of a Search Engine Text analysis
Architecture of a Search Engine (Karaoke Version)
Architecture of a Search Engine (Karaoke Version)
Architecture of a Search Engine (Karaoke Version)
Architecture of a Search Engine (Karaoke Version) Acquisition
Architecture of a Search Engine (Karaoke Version) Text analysis
Architecture of a Search Engine (Karaoke Version) Text analysis
Architecture of a Search Engine (Karaoke Version) Text analysis
Architecture of a Search Engine (Karaoke Version) Text analysis
Architecture of a Search Engine (Karaoke Version) Text analysis
Architecture of a Search Engine (Karaoke Version) Text analysis
Architecture of a Search Engine (Karaoke Version) Text analysis
Architecture of a Search Engine (Karaoke Version) Text analysis
Chapter IR:III III. Indexing
Indexing Basics Definition 1 (Index
Indexing Basics Definition 1 (Index
Indexing Basics Definition 1 (Index
Indexing Basics Requirements
Indexing Basics Requirements
Indexing Basics Requirements
Indexing Basics Requirements
Indexing Basics Requirements
Indexing Basics Requirements
Remarks: ❑
Indexing Basics Definition 2 (Document, Documentary Unit
Indexing Basics Definition 2 (Document, Documentary Unit
Indexing Basics Definition 2 (Document, Documentary Unit
Indexing Basics Definition 3 (Vocabulary, Terminology, Index Term
Indexing Basics Definition 3 (Vocabulary, Terminology, Index Term
Indexing Basics Definition 3 (Vocabulary, Terminology, Index Term
Indexing Basics Definition 3 (Vocabulary, Terminology, Index Term
Indexing Basics Definition 3 (Vocabulary, Terminology, Index Term
Remarks: ❑
Indexing Basics Querying an Index
Chapter IR:III III. Indexing
Inverted Index Term-Document Matrix
Inverted Index Term-Document Matrix
Inverted Index Term-Document Matrix
Inverted Index Term-Document Matrix
Inverted Index Term-Document Matrix
Inverted Index Term-Document Matrix
Inverted Index Term-Document Matrix
Inverted Index Term-Document Matrix
Inverted Index Term-Document Matrix
Inverted Index Data Structure
Inverted Index Data Structure
Inverted Index Posting
Inverted Index Posting
Inverted Index Posting
Inverted Index Posting List, Postlist
Remarks: ❑
Chapter IR:III III. Indexing
Query Processing I Retrieval Types
Query Processing I Query Semantics for Set Retrieval
Remarks: ❑
Remarks: ❑
Query Processing I Conjunctive Multi-Term Queries
Query Processing I Conjunctive Multi-Term Queries
Query Processing I List Intersection
Query Processing I List Intersection
Query Processing I List Intersection
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Query Processing I List Intersection: Example
Remarks: ❑
Query Processing I List Intersection
Query Processing I List Intersection
Query Processing I Proximity Queries
Query Processing I Proximity Queries
Query Processing I Proximity Queries
Query Processing I Proximity Queries
Query Processing I Phrase Queries
Query Processing I Phrase Queries
Query Processing I Phrase Queries
Query Processing I Phrase Queries
Query Processing I Phrase Queries
Remarks: ❑
Chapter IR:III III. Indexing
Query Processing II Retrieval Types
Query Processing II Query Semantics for Ranked Retrieval
Query Processing II Single-Term Queries
Query Processing II Single-Term Queries
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Remarks: ❑
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Query Processing II Document Scoring
Remarks: ❑
Query Processing II Document Scoring
Remarks: ❑
Query Processing II Top-k Retrieval
Query Processing II Index Distribution
Query Processing II Caching
Chapter IR:III III. Indexing
Index Construction In-Memory Index Construction
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Index Construction Index Merging
Remarks: ❑
Index Construction Distributed Indexing
Index Construction Distributed Indexing
Index Construction Distributed Indexing
Remarks: ❑ Computer clusters are often built from inexpensive
Index Construction Distributed Indexing
Index Construction Distributed Indexing: Example Netspeak
Index Construction Index Updates
Chapter IR:III III. Indexing
Size Estimation Query Result Set Size
Remarks: ❑
Size Estimation Query Result Set Size: Joint Probability
Size Estimation Query Result Set Size: Joint Probability
Size Estimation Query Result Set Size: Joint Probability
Size Estimation Query Result Set Size: Conditional Probability
Size Estimation Query Result Set Size: Conditional Probability
Size Estimation Query Result Set Size: Initial Result Set-based Estimation
Size Estimation Query Result Set Size: Initial Result Set-based Estimation
Size Estimation Indexed Collection Size: Joint Probability-based
Size Estimation Indexed Collection Size: Proportionality
Size Estimation Indexed Collection Size: Proportionality
Remarks: 1.
Remarks: (continued) 21.
Chapter IR:IV IV. Retrieval Models
Overview of Retrieval Models Document Views
Overview of Retrieval Models Retrieval Models
Overview of Retrieval Models Retrieval Models
Overview of Retrieval Models Retrieval Models
Overview of Retrieval Models Retrieval Models
Overview of Retrieval Models Definition 1 (Retrieval Model, Relevance Function)
Overview of Retrieval Models Definition 1 (Retrieval Model, Relevance Function)
Remarks: ❑
Overview of Retrieval Models Ranking Principles in IR
Overview of Retrieval Models Types of Retrieval Models
Overview of Retrieval Models Probability Ranking Principle (PRP)
Overview of Retrieval Models Probability Ranking Principle (PRP)
Remarks: ❑
Overview of Retrieval Models History of Retrieval Models
Overview of Retrieval Models Document Modeling
Chapter IR:IV IV. Retrieval Models
Boolean Retrieval Retrieval Model R = ⟨D, Q, ρ⟩
Boolean Retrieval Retrieval Model R = ⟨D, Q, ρ⟩
Remarks: ❑
Boolean Retrieval Relevance Function ρ
Boolean Retrieval Relevance Function ρ
Boolean Retrieval Example
Remarks: ❑
Boolean Retrieval Query Refinement: “Searching by Numbers”
Boolean Retrieval Query Refinement: “Searching by Numbers”
Boolean Retrieval Discussion
Vector Space Model Retrieval Model R = ⟨D, Q, ρ⟩
Vector Space Model Retrieval Model R = ⟨D, Q, ρ⟩
Vector Space Model Relevance Function ρ: Cosine Similarity
Vector Space Model Relevance Function ρ: Cosine Similarity
Vector Space Model Example
Vector Space Model Example
Vector Space Model Example
Vector Space Model Term Weighting: tf ·idf
Vector Space Model Term Weighting: tf ·idf
Vector Space Model Term Weighting: tf ·idf
Vector Space Model Term Weighting: tf ·idf
Vector Space Model Term Weighting: tf ·idf
Remarks: ❑
Vector Space Model Query Refinement: Relevance Feedback
Vector Space Model Query Refinement: Relevance Feedback
Vector Space Model Discussion
Chapter IR:IV IV. Retrieval Models
Binary Independence Model Retrieval Model R = ⟨D, Q, ρ⟩
Binary Independence Model Retrieval Model R = ⟨D, Q, ρ⟩
Remarks: ❑
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Derivation
Binary Independence Model Relevance Function ρ: Estimation
Binary Independence Model Relevance Function ρ: Estimation
Remarks: ❑
Binary Independence Model Relevance Function ρ: Example
Binary Independence Model Relevance Function ρ: Example
Binary Independence Model Relevance Function ρ: Example
Binary Independence Model Relevance Function ρ: Example
Binary Independence Model Relevance Function ρ: Example
Binary Independence Model Relevance Function ρ: Example
Binary Independence Model Relevance Function ρ: Example
Binary Independence Model Relevance Function ρ: Summary
Remarks: ❑
Binary Independence Model Query Refinement: Relevance Feedback
Binary Independence Model Query Refinement: Relevance Feedback Example
Binary Independence Model Query Refinement: Relevance Feedback
Binary Independence Model Query Refinement: Relevance Feedback
Remarks: ❑
Binary Independence Model Discussion
Okapi BM25 Retrieval Model R = ⟨D, Q, ρ⟩
Okapi BM25 Background
Remarks: ❑
Okapi BM25 Term Weighting
Okapi BM25 Term Weighting
Okapi BM25 Term Weighting
Okapi BM25 Term Weighting
Okapi BM25 Term Weighting
Okapi BM25 Term Weighting
Okapi BM25 Term Weighting
Okapi BM25 Term Weighting
Okapi BM25 Term Weighting
Okapi BM25 Discussion
Chapter IR:IV IV. Retrieval Models
Hidden Variable Models The terms in a document d ∈ D are related to its semantics.
Hidden Variable Models
Hidden Variable Models Term-Document Matrix
Hidden Variable Models Term-Document Matrix
Hidden Variable Models Term-Document Matrix
Hidden Variable Models Term-Document Matrix
Remarks:
Hidden Variable Models Term-Document Matrix
Latent Semantic Indexing Singular Value Decomposition
Latent Semantic Indexing Singular Value Decomposition
Latent Semantic Indexing Singular Value Decomposition
Latent Semantic Indexing Singular Value Decomposition
Latent Semantic Indexing Singular Value Decomposition
Latent Semantic Indexing Singular Value Decomposition
Latent Semantic Indexing Singular Value Decomposition
Latent Semantic Indexing Singular Value Decomposition
Remarks:
Remarks: (continued)
Latent Semantic Indexing Retrieval Model R = hD, Q, ρi
Latent Semantic Indexing Retrieval Model R = hD, Q, ρi
Latent Semantic Indexing Example 1
Latent Semantic Indexing Example 1
Latent Semantic Indexing Example 1
Latent Semantic Indexing Example 1: Term-Document Matrix A
Latent Semantic Indexing Example 1: Singular Value Decomposition A = U S V T
Latent Semantic Indexing Example 1: Singular Value Decomposition A = U S V T
Latent Semantic Indexing Example 1: Singular Value Decomposition A = U S V T
Latent Semantic Indexing Example 1: Dimensionality Reduction Ak = Uk Sk VkT
Latent Semantic Indexing Example 1: Dimensionality Reduction Ak = Uk Sk VkT
Latent Semantic Indexing Example 1: Dimensionality Reduction Ak = Uk Sk VkT
Latent Semantic Indexing Example 1: Retrieval in Concept Space
Latent Semantic Indexing Retrieval Model R = hD, Q, ρi
Latent Semantic Indexing Retrieval Model R = hD, Q, ρi
Latent Semantic Indexing Example 2
Latent Semantic Indexing Example 2
Latent Semantic Indexing Example 2
Remarks:
Latent Semantic Indexing Example 2: Document Similarity Matrix AT A
Latent Semantic Indexing Example 2: Document Similarity Matrix AT A
Latent Semantic Indexing Example 2: Document Similarity Matrix AT A
Latent Semantic Indexing Example 2: Term Similarity Matrix AAT
Latent Semantic Indexing Example 2: Term Similarity Matrix AAT
Latent Semantic Indexing Example 2: Term Similarity Matrix AAT
Latent Semantic Indexing Discussion
Explicit Semantic Analysis Concept Hypothesis
Explicit Semantic Analysis Concept Hypothesis
Explicit Semantic Analysis Concept Hypothesis
Explicit Semantic Analysis Retrieval Model R = hD, Q, ρi
Explicit Semantic Analysis Retrieval Model R = hD, Q, ρi
Explicit Semantic Analysis Document Representation
Explicit Semantic Analysis Relevance Function ρ
Explicit Semantic Analysis Relevance Function ρ
Explicit Semantic Analysis Discussion
Chapter IR:IV IV. Retrieval Models
Generative Models
Language Models Background
Language Models Basics: Grammar
Language Models Basics: Grammar
Remarks [Kastens 2005] :
Language Models Basics: Grammar
Remarks:
Language Models Basics: Grammar
Language Models Basics: Grammar
Remarks:
Language Models Basics: Chomsky Hierarchy
Language Models Basics: Chomsky Hierarchy
Language Models Basics: Chomsky Hierarchy
Language Models Basics: Chomsky Hierarchy
Remarks:
Language Models Basics: Calculi
Language Models Basics: Calculi
Language Models Example: Deterministic Language Model
Language Models Example: Deterministic Language Model
Language Models Example: Deterministic Language Model
Language Models Example: Deterministic Language Model
Remarks:
Language Models Example: Statistical Language Model
Language Models Example: Statistical Language Model
Remarks:
Language Models Retrieval Model R = hD, Q, ρi
Language Models Retrieval Model R = hD, Q, ρi
Language Models Relevance Function ρ: Derivation
Language Models Relevance Function ρ: Derivation
Language Models Relevance Function ρ: Derivation
Language Models Relevance Function ρ: Derivation
Language Models Relevance Function ρ: Derivation
Language Models Relevance Function ρ: Derivation
Language Models Relevance Function ρ: Derivation
Language Models Relevance Function ρ: Derivation
Language Models Relevance Function ρ: Estimation
Language Models Relevance Function ρ: Estimation
Language Models Relevance Function ρ: Estimation
Remarks:
Language Models Relevance Function ρ: Estimation
Language Models Relevance Function ρ: Estimation
Language Models Relevance Function ρ: Estimation
Remarks:
Language Models Relevance Function ρ: Example
Language Models Relevance Function ρ: Example
Language Models Relevance Function ρ: Summary
Language Models Query Refinement: Relevance Feedback
Language Models Query Refinement: Relevance Feedback
Language Models Query Refinement: Relevance Feedback
Language Models Query Refinement: Relevance Feedback
Remarks:
Language Models Query Refinement: Relevance Feedback
Language Models Query Refinement: Relevance Feedback
Language Models Query Refinement: Relevance Feedback
Language Models Query Refinement: Relevance Feedback
Language Models Query Refinement: Relevance Feedback
Language Models Query Refinement: Relevance Feedback
Language Models Discussion
Chapter IR:V V. Users and Queries
Information Needs and Queries Information Needs
Information Needs and Queries Queries
Information Needs and Queries Interaction
Information Needs and Queries ASK Hypothesis
Information Needs and Queries Keyword Queries
Information Needs and Queries Keyword Queries
Chapter IR:VII VII. Evaluation
Laboratory Experiments Retrieval Types
Remarks:
Laboratory Experiments Experimental Setup
Remarks:
Laboratory Experiments Experimental Setup: Document Collections \u002f Corpora
Remarks:
Laboratory Experiments Experimental Setup: Topics
Remarks:
Laboratory Experiments Experimental Setup: Relevance Judgments
Laboratory Experiments Experimental Setup: Relevance Judgments
Laboratory Experiments Experimental Setup: Relevance Judgments
Laboratory Experiments Experimental Setup: Relevance Judgments
Laboratory Experiments Experimental Setup: Relevance Judgments
Remarks:
Laboratory Experiments Experimental Setup: Pooling
Laboratory Experiments Assessor Reliability
Laboratory Experiments Assessor Reliability: Kappa Statistics
Laboratory Experiments Assessor Reliability: Kappa Statistics
Laboratory Experiments Assessor Reliability: Kappa Statistics
Remarks:
Chapter IR:VII VII. Evaluation
Measuring Performance Effectiveness and Efficiency
Measuring Performance Effectiveness Measures
Measuring Performance Effectiveness Measures
Measuring Performance Effectiveness Measures
Measuring Performance Effectiveness Measures
Measuring Performance Effectiveness Measures
Set Retrieval Effectiveness Precision and Recall
Set Retrieval Effectiveness Precision and Recall
Remarks:
Set Retrieval Effectiveness F -Measure
Set Retrieval Effectiveness F -Measure
Remarks:
Remarks (ctd.):
Set Retrieval Effectiveness Illustration
Set Retrieval Effectiveness Illustration
Set Retrieval Effectiveness Illustration
Set Retrieval Effectiveness Precision and Recall Averaging
Set Retrieval Effectiveness Precision and Recall Averaging
Set Retrieval Effectiveness Precision and Recall Averaging
Remarks:
Set Retrieval Effectiveness Recall Estimation
Ranked Retrieval Effectiveness Example
Ranked Retrieval Effectiveness Precision@k and Recall@k
Ranked Retrieval Effectiveness Precision@k and Recall@k
Ranked Retrieval Effectiveness Precision-Recall Curves
Ranked Retrieval Effectiveness Precision-Recall Curves
Ranked Retrieval Effectiveness Precision-Recall Curves
Ranked Retrieval Effectiveness Average Precision
Ranked Retrieval Effectiveness Average Precision
Ranked Retrieval Effectiveness Average Precision
Ranked Retrieval Effectiveness Average Precision
Ranked Retrieval Effectiveness Average Precision (Alternative 1)
Ranked Retrieval Effectiveness Average Precision (Alternative 2)
Ranked Retrieval Effectiveness Average Precision
Ranked Retrieval Effectiveness Averaging Precision-Recall Curves
Ranked Retrieval Effectiveness Averaging Precision-Recall Curves
Ranked Retrieval Effectiveness Averaging Precision-Recall Curves
Ranked Retrieval Effectiveness Averaging Precision-Recall Curves
Ranked Retrieval Effectiveness Averaging Precision-Recall Curves
Ranked Retrieval Effectiveness Averaging Precision-Recall Curves
Ranked Retrieval Effectiveness Mean Average Precision (MAP)
Ranked Retrieval Effectiveness Mean Average Precision (MAP)
Ranked Retrieval Effectiveness Mean Average Precision (MAP)
Ranked Retrieval Effectiveness Mean Average Precision (MAP)
Ranked Retrieval Effectiveness Mean Reciprocal Rank (MRR)
Remarks: [Fuhr 2017]
Ranked Retrieval Effectiveness Discounted Cumulative Gain (DCG)
Ranked Retrieval Effectiveness Normalized Discounted Cumulative Gain (nDCG)
Remarks:
Chapter IR:VII VII. Evaluation
Training and Testing Statistical Hypothesis Testing
Training and Testing Statistical Hypothesis Testing
Training and Testing Statistical Hypothesis Testing
Training and Testing Statistical Hypothesis Testing
Training and Testing Statistical Hypothesis Testing
Training and Testing Statistical Hypothesis Testing
Remarks:
Training and Testing Statistical Hypothesis Testing: Sign Test
Training and Testing Statistical Hypothesis Testing: Sign Test
Training and Testing Statistical Hypothesis Testing: Sign Test
Training and Testing Statistical Hypothesis Testing: Student’s t-test
Training and Testing Statistical Hypothesis Testing: Student’s t-test
Training and Testing Statistical Hypothesis Testing: Student’s t-test
Training and Testing Statistical Hypothesis Testing: Power and Effect Size
Remarks:
Training and Testing Hyperparameter Optimization
Chapter IR:XI XI. IR Applications
Web Technology Overview
Web Technology Internet
Web Technology Internet
Web Technology Internet
Web Technology World Wide Web
Web Technology World Wide Web
Web Technology World Wide Web
Web Technology Addressing
Web Technology Addressing
Web Technology Addressing
Web Technology Hypertext Transfer Protocol (HTTP)
Web Technology Hypertext Transfer Protocol (HTTP)
Web Technology Hypertext Transfer Protocol (HTTP)
Web Technology Hypertext Markup Language (HTML)
Web Graph Topology of the Web
Web Graph Topology of the Web
Web Graph Topology of the Web
Web Crawling Crawling Hypertext
Web Crawling Requirements
Web Crawling Selectivity
Remarks:
Web Crawling Selectivity: Malicious Pages (Black-hat SEO∗, Spam)
Web Crawling Politeness
Web Crawling Politeness: Robots Exclusion Protocol (robots.txt)
Remarks:
Web Crawling Politeness: Crawling Algorithm Revisited
Web Crawling Freshness
Web Crawling Freshness: Metric
Web Crawling Freshness: Metric
Web Crawling Freshness: Age Metric
Web Crawling Freshness: Age Metric
Web Crawling Freshness: Age Metric
Web Crawling Freshness: Age Metric
Web Crawling Freshness: Age Metric
Web Crawling Efficiency and Scalability
Remarks:
Web Crawling Extensibility
Link Analysis Hyperlinks
Link Analysis Anchor Text
Remarks:
Link Analysis PageRank
Link Analysis PageRank: Random Surfer Model
Link Analysis PageRank: Definition
Link Analysis PageRank: Example
Link Analysis Algorithm:
Link Analysis Algorithm:
Link Analysis Algorithm:
Link Analysis PageRank: Convergence
Link Analysis PageRank: Variants
Chapter IR:XI XI. IR Applications
The Treachery of Answers Retrieving answers as a retrieval paradigm:
The Treachery of Answers Retrieving answers as a retrieval paradigm:
The Treachery of Answers Retrieving answers as a retrieval paradigm:
The Treachery of Answers
IR:XI-58
Remarks:
Argument Retrieval Problems Basic Argument Model
Argument Retrieval Problems Basic Argument Model
Argument Retrieval Problems Basic Argument Model
Argument Retrieval Problems Basic Argument Model
Argument Retrieval Problems Basic Argument Model
Argument Retrieval Problems Basic Argument Model
Argument Retrieval Problems Basic Argument Model
Argument Retrieval Problems Basic Argument Model
Argument Retrieval Problems (1) Argument Relevance Πrel
Argument Retrieval Problems (1) Argument Relevance Πrel
Argument Retrieval Problems (1) Argument Relevance Πrel
Argument Retrieval Problems (2) Argument Ranking Πrank
Argument Retrieval Problems (2) Argument Ranking Πrank
Argument Retrieval Problems (3) – (7) Further Problems
Argument Retrieval Problems (3) – (7) Further Problems
Chapter IR:XI XI. IR Applications
Argument Ranking I
Argument Ranking I
Argument Ranking I Query Reintroduce death penalty?
Argument Ranking I Query Reintroduce death penalty?
Argument Ranking I Query Reintroduce death penalty?
Argument Ranking I Query Reintroduce death penalty?
Argument Ranking I Query Reintroduce death penalty?
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I X p(dj )
Argument Ranking I From Premise Scores to Argument Ranks
Argument Ranking I From Premise Scores to Argument Ranks
Argument Ranking I Case Study: Graph Construction
Argument Ranking I Case Study: Graph Construction
Argument Ranking I Case Study: Results
Argument Ranking I Case Study: Results
Argument Ranking I Case Study: Results
Argument Ranking I Case Study: Results
Argument Ranking I Case Study: Results
Argument Ranking I Case Study: Results
Argument Ranking II
Argument Ranking II
Argument Ranking II ∗
Argument Ranking II ∗
Argument Ranking II Corpus and Analysis
Argument Ranking II Corpus and Analysis
Chapter IR:XI XI. IR Applications
Argumentation-Related Resources
Argumentation-Related Resources ∗
Argumentation-Related Resources ∗
Argumentation-Related Resources ∗
Argumentation-Related Resources ∗
Argumentation-Related Resources ∗
Argumentation-Related Resources ∗
Argumentation-Related Resources The Argument Web
Argumentation-Related Resources The Argument Web
Argument Search Engines Vision of Argument Search
Argument Search Engines* Vision of Argument Search
Argument Search Engines Basic Elements and Process
Argument Search Engines Basic Elements and Process
Argument Search Engines Basic Elements and Process
Argument Search Engines Basic Elements and Process
Argument Search Engines Acquisition Paradigms: (a) args.me
Argument Search Engines Acquisition Paradigms: (b) IBM Debater
Argument Search Engines Acquisition Paradigms: (c) ArgumenText
Argument Search Engines Ranking Paradigms in IR
Argument Search Engines Ranking Paradigms in IR
Argument Search Engines Ranking Paradigms in IR
Argument Search Engines More on Args
Argument Search Engines More on Args
Argument Search Engines Presentation and Analytics
Argument Search Engines Presentation and Analytics
Argument Search Engines Presentation and Analytics
Chapter IR:XI XI. IR Applications
SameSide @ ArgMining 2019 1st Shared Task on Same Side Stance Classification
Argument Search Evaluation I Same Side Stance Classification
Argument Search Evaluation I Same Side Stance Classification
Argument Search Evaluation I Same Side Stance Classification
Argument Search Evaluation I Same Side Stance Classification: Task Rationale
Argument Search Evaluation I Same Side Stance Classification: Tasks Details
Argument Search Evaluation I Same Side Stance Classification: Tasks Details
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Within Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Cross Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Cross Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Cross Domain”
Argument Search Evaluation I Same Side Stance Classification: Results “Cross Domain”
Touché @ CLEF 2020 1st Shared Task on Argument Retrieval
Argument Search Evaluation II Argument Retrieval Task @ CLEF 2020
Argument Search Evaluation II Argument Retrieval Task @ CLEF 2020
Argument Search Evaluation II Supporting Argumentative Conversations: Results
Argument Search Evaluation II Supporting Argumentative Conversations: Results
Argument Search Evaluation II Supporting Argumentative Conversations: Results
Argument Search Evaluation II Supporting Argumentative Conversations: Results
Argument Search Evaluation II Supporting Argumentative Conversations: Results
Argument Search Evaluation II Supporting Argumentative Conversations: Results
Argument Search Evaluation II Supporting Argumentative Conversations: Results
Argument Search Evaluation II Answering Comparative Questions with Arguments: Results
Argument Search Evaluation II Answering Comparative Questions with Arguments: Results
Argument Search Evaluation II Answering Comparative Questions with Arguments: Results
Argument Search Evaluation II Answering Comparative Questions with Arguments: Results
Argument Search Evaluation II Answering Comparative Questions with Arguments: Results
Argument Search Evaluation II Answering Comparative Questions with Arguments: Results
Argument Search Evaluation II Answering Comparative Questions with Arguments: Results