Natural Language Processing. Topics in Information Retrieval. Updated 5/10
|
|
- Helena Campbell
- 5 years ago
- Views:
Transcription
1 Natural Language Processing Topics in Information Retrieval Updated 5/10
2 Outline Introduction to IR Design features of IR systems Evaluation measures The vector space model Latent semantic indexing
3 Background on IR Retrieve textual information from document repositories. What is unstructured data? Scales of information retrieval systems Searching the web Searching document repositories (e.g. of an enterprise) Searching documents of a personal computer
4 Background on IR Ad-hoc retrieval: User enters a query describing the desired information The system returns a list of documents. Two main models: exact match (e.g. for Boolean queries) somewhat older ranked list
5 Text Categorization Attempt to assign documents to two or more pre-defined categories. Routing: Ranking of documents according to relevance. Training information in the form of relevance labels is available. Filtering: Absolute assessment of relevance.
6 Design Features of IR Systems Inverted Index: Primary data structure of IR systems. An inverted index lists for each word the documents that contain it and its frequency of occurrence. Including the position information allows searching for phrases. Stop List (Function Words): Lists words unlikely to be useful for searching. Examples: the, on, could. Excluding this considerably reduces the size of the inverted index without significantly affecting its performance. However, this would make it impossible to search for phrases that contain stop words.
7 Design Features (Cont.) Stemming: Simplified form of morphological analysis consisting simply of truncating a word. For example laughing, laughs, laugh and laughed are all stemmed to laugh. The problem is semantically different words like gallery and gall may both be truncated to gall making the stems unintelligible to users.
8 Evaluation Measures Precision: Percentage of relevant items returned. Recall: Percentage of all relevant documents in the collection that is in the returned set. Combine precision and recall: Cutoff precision at a particular cutoff, e.g. precision at 5 Uninterpolated average precision: precision values are averaged for points with relevant documents. Interpolated average precision : likewise interpolated. Precision-Recall curves F measure
9 Evaluation Measures example for three rankings
10 Un-interpolated & interpolated average precision
11 Probability Ranking Principle (PRP) Ranking documents in order of decreasing probability of relevance is optimal. View retrieval as a greedy search that aims to identify the most valuable document. Assumptions of PRP: Documents are independent. Complex information need is broken into a number of queries which are each optimized in isolation. Probability of relevance is only estimated.
12 The Vector Space Model Measure closeness between query and document. Queries and documents represented as n dimensional vectors. Each dimension corresponds to a word. Advantages: Conceptual simplicity and use of spatial proximity for semantic proximity.
13 Vector Similarity d = The man said that a space age man appeared d = Those men appeared to say their age
14 Vector Similarity (Cont.) cosine measure or normalized correlation coefficient Euclidean Distance:
15 Term Weighting Quantities used: tf i,j (Term frequency) : # of occurrences of w i in d i df i (Document frequency) : # of documents that w i occurs in cf i (Collection frequency) : total # of occurrences of w i in the collection
16 Term Weighting (Cont.) tf i,j = 1+log(tf), tf > 0 df i : indicator of informativeness Inverse document frequency (IDF weighting) TF.IDF (Term frequency & Inverse Document Frequency): indicator of semantically focused words: weight( i, j) = (1+ 0 log( tf i, j )) log N df i if if tf tf i, j i, j 1 = 0
17 Normalization Normalization is considered essential im many weighting schemes, otherwise longer documents would tend to be ranked higher.
18 Term Distribution Models Develop a model for the distribution of a word and use this model to characterize its importance for retrieval. Estimate p i (k): p i (k) : proportion of times that word w i appears k times in document. Poisson, Two-Poisson and K mixture. We can derive the IDF from term distribution models.
19 The Poisson Distribution λ ( ; ) = i λ p k λ i for some > i e λi 0 k! k the parameter λi > 0 is the average number of occurrences of w i per document. λ i = cf i N We are interested in the frequency of occurrence of a particular word w i in a document. Poisson distribution is good for estimating noncontent words.
20 The Two-Poisson Model Better fit to the frequency distribution Mixture of two poissons Non-privileged class: Low average # of occurrences Occurrences are accidental Privileged class: High average # of occurrences Central content word p π λ 1 λ ( k;,, ) e 1 k (1 ) e 1 λ 2 = π λ k! π λ π : probability of a document being in the privileged class 1-π : probability of a document being in the non-privileged class + 2 λ k 2 k! λ 1, λ 2 : average number of occurrence of word w i in each class
21 The K Mixture More accurate p k α δ i( ) = (1 ) k, 0 + α β β+ 1 β+ 1 k λ= cf N IDF= log2 N df β =λ 2 IDF 1= cf - df df α = λ β β : # of extra terms per document in which the term occurs α : absolute frequency of the term.
22 Latent Semantic Indexing Projects queries and documents into a space with latent semantic dimensions. Dimensionality reduction: the latent semantic space that we project into has fewer dimensions than the original space. Exploits co-occurrence: the fact that two or more terms occur in the same document more often than chance. Similarity metric: Co-occurring terms are projected onto the same dimensions.
23 Singular Value Decomposition SVD takes a document-by-term matrix A in n- dim space and projects it to A^ in a lower dimensional space k (n>>k), namely lower rank matrix, such that the 2-norm (distance) between the two matrices is minimized: = A Â 2
24 SVD (Cont) SVD projection: A T S ) t d = t n n n( Dd n T A txd term by document matrix T txn Terms in new space S nxn Singular values of A in descending order D dxn document matrix in new space n = min (t,d) T, D have orthonormal columns Fewer terms may be retained to achieve dimensionality reduction
25 LSI in IR Encode terms and documents using factors derived from SVD. Rank similarity of terms and docs to query via Euclidean distances or cosines.
26 LSI example
27 LSI example cont. T S D
28 LSI example : original vs. dimension reduced A = k = k =
29 LSI example cont. Condensed representation of documents B=S 2*2 D 2*n = cosines
30 LSI example - querying q = q T T k S k -1 For example: q= astronaut car =( ) T q = ( ) T Query result cos(q,b i ) = ( )
31 Latent semantic indexing in IR The application of SVD to IR is called Latent Semantic Indexing (LSI) Comparing LSI to standard vector space search Higher recall Reduced precision The latency comes form the fact that original terms are transformed to a new basis, thought to be the true representation of the data. Is SVD representation more efficientt? Seems to be, due to compression. E.g if one reduces to 150 dimensions But needs costly matrix computations. Inverted indexing is not possible! Effort for computing the SVD. Objection to SVD: SVD is really designed for normal distributions, but count data is evidently not normal.
32 Discourse Segmentation Break documents into topically coherent multi-paragraph subparts. Detect topic shifts within document
33 TextTiling (Hearst and Plaunt, 1993) Search for vocabulary shifts from one subtopic to another. Divide text into fixed size blocks (20 words). Look for topic shifts in-between these blocks. Cohesion scorer: measures the topic continuity at each gap (point between two block). Depth scorer: at a gap determine how low the cohesion score is compared to surrounding gaps. Boundary selector: looks at the depth scores & selects the gaps that are the best segmentation points.
34 Three constellations of cohesion scores
Manning & Schuetze, FSNLP, (c)
page 554 554 15 Topics in Information Retrieval co-occurrence Latent Semantic Indexing Term 1 Term 2 Term 3 Term 4 Query user interface Document 1 user interface HCI interaction Document 2 HCI interaction
More informationManning & Schuetze, FSNLP (c) 1999,2000
558 15 Topics in Information Retrieval (15.10) y 4 3 2 1 0 0 1 2 3 4 5 6 7 8 Figure 15.7 An example of linear regression. The line y = 0.25x + 1 is the best least-squares fit for the four points (1,1),
More informationLatent semantic indexing
Latent semantic indexing Relationship between concepts and words is many-to-many. Solve problems of synonymy and ambiguity by representing documents as vectors of ideas or concepts, not terms. For retrieval,
More informationVariable Latent Semantic Indexing
Variable Latent Semantic Indexing Prabhakar Raghavan Yahoo! Research Sunnyvale, CA November 2005 Joint work with A. Dasgupta, R. Kumar, A. Tomkins. Yahoo! Research. Outline 1 Introduction 2 Background
More informationCS 3750 Advanced Machine Learning. Applications of SVD and PCA (LSA and Link analysis) Cem Akkaya
CS 375 Advanced Machine Learning Applications of SVD and PCA (LSA and Link analysis) Cem Akkaya Outline SVD and LSI Kleinberg s Algorithm PageRank Algorithm Vector Space Model Vector space model represents
More informationInformation Retrieval and Topic Models. Mausam (Based on slides of W. Arms, Dan Jurafsky, Thomas Hofmann, Ata Kaban, Chris Manning, Melanie Martin)
Information Retrieval and Topic Models Mausam (Based on slides of W. Arms, Dan Jurafsky, Thomas Hofmann, Ata Kaban, Chris Manning, Melanie Martin) Sec. 1.1 Unstructured data in 1620 Which plays of Shakespeare
More informationCS47300: Web Information Search and Management
CS47300: Web Information Search and Management Prof. Chris Clifton 6 September 2017 Material adapted from course created by Dr. Luo Si, now leading Alibaba research group 1 Vector Space Model Disadvantages:
More informationRetrieval by Content. Part 2: Text Retrieval Term Frequency and Inverse Document Frequency. Srihari: CSE 626 1
Retrieval by Content Part 2: Text Retrieval Term Frequency and Inverse Document Frequency Srihari: CSE 626 1 Text Retrieval Retrieval of text-based information is referred to as Information Retrieval (IR)
More informationScoring (Vector Space Model) CE-324: Modern Information Retrieval Sharif University of Technology
Scoring (Vector Space Model) CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2014 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford)
More informationScoring (Vector Space Model) CE-324: Modern Information Retrieval Sharif University of Technology
Scoring (Vector Space Model) CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2016 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford)
More informationScoring (Vector Space Model) CE-324: Modern Information Retrieval Sharif University of Technology
Scoring (Vector Space Model) CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2017 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford)
More information.. CSC 566 Advanced Data Mining Alexander Dekhtyar..
.. CSC 566 Advanced Data Mining Alexander Dekhtyar.. Information Retrieval Latent Semantic Indexing Preliminaries Vector Space Representation of Documents: TF-IDF Documents. A single text document is a
More informationVector Space Model. Yufei Tao KAIST. March 5, Y. Tao, March 5, 2013 Vector Space Model
Vector Space Model Yufei Tao KAIST March 5, 2013 In this lecture, we will study a problem that is (very) fundamental in information retrieval, and must be tackled by all search engines. Let S be a set
More informationMachine Learning. Principal Components Analysis. Le Song. CSE6740/CS7641/ISYE6740, Fall 2012
Machine Learning CSE6740/CS7641/ISYE6740, Fall 2012 Principal Components Analysis Le Song Lecture 22, Nov 13, 2012 Based on slides from Eric Xing, CMU Reading: Chap 12.1, CB book 1 2 Factor or Component
More informationLatent Semantic Models. Reference: Introduction to Information Retrieval by C. Manning, P. Raghavan, H. Schutze
Latent Semantic Models Reference: Introduction to Information Retrieval by C. Manning, P. Raghavan, H. Schutze 1 Vector Space Model: Pros Automatic selection of index terms Partial matching of queries
More informationBoolean and Vector Space Retrieval Models
Boolean and Vector Space Retrieval Models Many slides in this section are adapted from Prof. Joydeep Ghosh (UT ECE) who in turn adapted them from Prof. Dik Lee (Univ. of Science and Tech, Hong Kong) 1
More informationMatrix Factorization & Latent Semantic Analysis Review. Yize Li, Lanbo Zhang
Matrix Factorization & Latent Semantic Analysis Review Yize Li, Lanbo Zhang Overview SVD in Latent Semantic Indexing Non-negative Matrix Factorization Probabilistic Latent Semantic Indexing Vector Space
More informationRETRIEVAL MODELS. Dr. Gjergji Kasneci Introduction to Information Retrieval WS
RETRIEVAL MODELS Dr. Gjergji Kasneci Introduction to Information Retrieval WS 2012-13 1 Outline Intro Basics of probability and information theory Retrieval models Boolean model Vector space model Probabilistic
More informationSingular Value Decompsition
Singular Value Decompsition Massoud Malek One of the most useful results from linear algebra, is a matrix decomposition known as the singular value decomposition It has many useful applications in almost
More informationInformation Retrieval
Introduction to Information CS276: Information and Web Search Christopher Manning and Pandu Nayak Lecture 13: Latent Semantic Indexing Ch. 18 Today s topic Latent Semantic Indexing Term-document matrices
More informationLatent Semantic Indexing (LSI) CE-324: Modern Information Retrieval Sharif University of Technology
Latent Semantic Indexing (LSI) CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2014 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276,
More informationInformation Retrieval
Introduction to Information Retrieval CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan Lecture 6: Scoring, Term Weighting and the Vector Space Model This lecture;
More informationIntroduction to Information Retrieval (Manning, Raghavan, Schutze) Chapter 6 Scoring term weighting and the vector space model
Introduction to Information Retrieval (Manning, Raghavan, Schutze) Chapter 6 Scoring term weighting and the vector space model Ranked retrieval Thus far, our queries have all been Boolean. Documents either
More informationLatent Semantic Indexing (LSI) CE-324: Modern Information Retrieval Sharif University of Technology
Latent Semantic Indexing (LSI) CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2016 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276,
More information9 Searching the Internet with the SVD
9 Searching the Internet with the SVD 9.1 Information retrieval Over the last 20 years the number of internet users has grown exponentially with time; see Figure 1. Trying to extract information from this
More informationLatent Semantic Analysis. Hongning Wang
Latent Semantic Analysis Hongning Wang CS@UVa Recap: vector space model Represent both doc and query by concept vectors Each concept defines one dimension K concepts define a high-dimensional space Element
More informationInformation Retrieval
Introduction to Information Retrieval CS276: Information Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan Lecture 6: Scoring, Term Weighting and the Vector Space Model This lecture; IIR Sections
More informationTerm Weighting and Vector Space Model. Reference: Introduction to Information Retrieval by C. Manning, P. Raghavan, H. Schutze
Term Weighting and Vector Space Model Reference: Introduction to Information Retrieval by C. Manning, P. Raghavan, H. Schutze 1 Ranked retrieval Thus far, our queries have all been Boolean. Documents either
More informationInformation Retrieval and Web Search
Information Retrieval and Web Search IR models: Vector Space Model IR Models Set Theoretic Classic Models Fuzzy Extended Boolean U s e r T a s k Retrieval: Adhoc Filtering Brosing boolean vector probabilistic
More informationTerm Weighting and the Vector Space Model. borrowing from: Pandu Nayak and Prabhakar Raghavan
Term Weighting and the Vector Space Model borrowing from: Pandu Nayak and Prabhakar Raghavan IIR Sections 6.2 6.4.3 Ranked retrieval Scoring documents Term frequency Collection statistics Weighting schemes
More informationvector space retrieval many slides courtesy James Amherst
vector space retrieval many slides courtesy James Allan@umass Amherst 1 what is a retrieval model? Model is an idealization or abstraction of an actual process Mathematical models are used to study the
More information1 Information retrieval fundamentals
CS 630 Lecture 1: 01/26/2006 Lecturer: Lillian Lee Scribes: Asif-ul Haque, Benyah Shaparenko This lecture focuses on the following topics Information retrieval fundamentals Vector Space Model (VSM) Deriving
More informationLecture 5: Web Searching using the SVD
Lecture 5: Web Searching using the SVD Information Retrieval Over the last 2 years the number of internet users has grown exponentially with time; see Figure. Trying to extract information from this exponentially
More information13 Searching the Web with the SVD
13 Searching the Web with the SVD 13.1 Information retrieval Over the last 20 years the number of internet users has grown exponentially with time; see Figure 1. Trying to extract information from this
More informationVector Space Scoring Introduction to Information Retrieval INF 141 Donald J. Patterson
Vector Space Scoring Introduction to Information Retrieval INF 141 Donald J. Patterson Content adapted from Hinrich Schütze http://www.informationretrieval.org Querying Corpus-wide statistics Querying
More informationChapter 10: Information Retrieval. See corresponding chapter in Manning&Schütze
Chapter 10: Information Retrieval See corresponding chapter in Manning&Schütze Evaluation Metrics in IR 2 Goal In IR there is a much larger variety of possible metrics For different tasks, different metrics
More informationGeneric Text Summarization
June 27, 2012 Outline Introduction 1 Introduction Notation and Terminology 2 3 4 5 6 Text Summarization Introduction Notation and Terminology Two Types of Text Summarization Query-Relevant Summarization:
More informationCS276A Text Information Retrieval, Mining, and Exploitation. Lecture 4 15 Oct 2002
CS276A Text Information Retrieval, Mining, and Exploitation Lecture 4 15 Oct 2002 Recap of last time Index size Index construction techniques Dynamic indices Real world considerations 2 Back of the envelope
More informationDealing with Text Databases
Dealing with Text Databases Unstructured data Boolean queries Sparse matrix representation Inverted index Counts vs. frequencies Term frequency tf x idf term weights Documents as vectors Cosine similarity
More informationRanked IR. Lecture Objectives. Text Technologies for Data Science INFR Learn about Ranked IR. Implement: 10/10/2018. Instructor: Walid Magdy
Text Technologies for Data Science INFR11145 Ranked IR Instructor: Walid Magdy 10-Oct-2018 Lecture Objectives Learn about Ranked IR TFIDF VSM SMART notation Implement: TFIDF 2 1 Boolean Retrieval Thus
More informationLinear Algebra Background
CS76A Text Retrieval and Mining Lecture 5 Recap: Clustering Hierarchical clustering Agglomerative clustering techniques Evaluation Term vs. document space clustering Multi-lingual docs Feature selection
More informationLatent Semantic Analysis. Hongning Wang
Latent Semantic Analysis Hongning Wang CS@UVa VS model in practice Document and query are represented by term vectors Terms are not necessarily orthogonal to each other Synonymy: car v.s. automobile Polysemy:
More informationSparse vectors recap. ANLP Lecture 22 Lexical Semantics with Dense Vectors. Before density, another approach to normalisation.
ANLP Lecture 22 Lexical Semantics with Dense Vectors Henry S. Thompson Based on slides by Jurafsky & Martin, some via Dorota Glowacka 5 November 2018 Previous lectures: Sparse vectors recap How to represent
More informationANLP Lecture 22 Lexical Semantics with Dense Vectors
ANLP Lecture 22 Lexical Semantics with Dense Vectors Henry S. Thompson Based on slides by Jurafsky & Martin, some via Dorota Glowacka 5 November 2018 Henry S. Thompson ANLP Lecture 22 5 November 2018 Previous
More informationInformation Retrieval
Introduction to Information Retrieval Lecture 11: Probabilistic Information Retrieval 1 Outline Basic Probability Theory Probability Ranking Principle Extensions 2 Basic Probability Theory For events A
More informationMatrices, Vector Spaces, and Information Retrieval
Matrices, Vector Spaces, and Information Authors: M. W. Berry and Z. Drmac and E. R. Jessup SIAM 1999: Society for Industrial and Applied Mathematics Speaker: Mattia Parigiani 1 Introduction Large volumes
More informationFall CS646: Information Retrieval. Lecture 6 Boolean Search and Vector Space Model. Jiepu Jiang University of Massachusetts Amherst 2016/09/26
Fall 2016 CS646: Information Retrieval Lecture 6 Boolean Search and Vector Space Model Jiepu Jiang University of Massachusetts Amherst 2016/09/26 Outline Today Boolean Retrieval Vector Space Model Latent
More informationINFO 4300 / CS4300 Information Retrieval. IR 9: Linear Algebra Review
INFO 4300 / CS4300 Information Retrieval IR 9: Linear Algebra Review Paul Ginsparg Cornell University, Ithaca, NY 24 Sep 2009 1/ 23 Overview 1 Recap 2 Matrix basics 3 Matrix Decompositions 4 Discussion
More informationRanked IR. Lecture Objectives. Text Technologies for Data Science INFR Learn about Ranked IR. Implement: 10/10/2017. Instructor: Walid Magdy
Text Technologies for Data Science INFR11145 Ranked IR Instructor: Walid Magdy 10-Oct-017 Lecture Objectives Learn about Ranked IR TFIDF VSM SMART notation Implement: TFIDF 1 Boolean Retrieval Thus far,
More informationVector Space Scoring Introduction to Information Retrieval Informatics 141 / CS 121 Donald J. Patterson
Vector Space Scoring Introduction to Information Retrieval Informatics 141 / CS 121 Donald J. Patterson Content adapted from Hinrich Schütze http://www.informationretrieval.org Querying Corpus-wide statistics
More informationWhat is Text mining? To discover the useful patterns/contents from the large amount of data that can be structured or unstructured.
What is Text mining? To discover the useful patterns/contents from the large amount of data that can be structured or unstructured. Text mining What can be used for text mining?? Classification/categorization
More informationBoolean and Vector Space Retrieval Models CS 290N Some of slides from R. Mooney (UTexas), J. Ghosh (UT ECE), D. Lee (USTHK).
Boolean and Vector Space Retrieval Models 2013 CS 290N Some of slides from R. Mooney (UTexas), J. Ghosh (UT ECE), D. Lee (USTHK). 1 Table of Content Boolean model Statistical vector space model Retrieval
More informationInvestigation of Latent Semantic Analysis for Clustering of Czech News Articles
Investigation of Latent Semantic Analysis for Clustering of Czech News Articles Michal Rott, Petr Červa Laboratory of Computer Speech Processing 4. 9. 2014 Introduction Idea of article clustering Presumptions:
More informationScoring, Term Weighting and the Vector Space
Scoring, Term Weighting and the Vector Space Model Francesco Ricci Most of these slides comes from the course: Information Retrieval and Web Search, Christopher Manning and Prabhakar Raghavan Content [J
More informationSemantic Similarity from Corpora - Latent Semantic Analysis
Semantic Similarity from Corpora - Latent Semantic Analysis Carlo Strapparava FBK-Irst Istituto per la ricerca scientifica e tecnologica I-385 Povo, Trento, ITALY strappa@fbk.eu Overview Latent Semantic
More informationVector Space Scoring Introduction to Information Retrieval INF 141 Donald J. Patterson
Vector Space Scoring Introduction to Information Retrieval INF 141 Donald J. Patterson Content adapted from Hinrich Schütze http://www.informationretrieval.org Collection Frequency, cf Define: The total
More informationQuerying. 1 o Semestre 2008/2009
Querying Departamento de Engenharia Informática Instituto Superior Técnico 1 o Semestre 2008/2009 Outline 1 2 3 4 5 Outline 1 2 3 4 5 function sim(d j, q) = 1 W d W q W d is the document norm W q is the
More informationPart I: Web Structure Mining Chapter 1: Information Retrieval and Web Search
Part I: Web Structure Mining Chapter : Information Retrieval an Web Search The Web Challenges Crawling the Web Inexing an Keywor Search Evaluating Search Quality Similarity Search The Web Challenges Tim
More informationRanked Retrieval (2)
Text Technologies for Data Science INFR11145 Ranked Retrieval (2) Instructor: Walid Magdy 31-Oct-2017 Lecture Objectives Learn about Probabilistic models BM25 Learn about LM for IR 2 1 Recall: VSM & TFIDF
More informationDATA MINING LECTURE 8. Dimensionality Reduction PCA -- SVD
DATA MINING LECTURE 8 Dimensionality Reduction PCA -- SVD The curse of dimensionality Real data usually have thousands, or millions of dimensions E.g., web documents, where the dimensionality is the vocabulary
More informationINFO 4300 / CS4300 Information Retrieval. slides adapted from Hinrich Schütze s, linked from
INFO 4300 / CS4300 Information Retrieval slides adapted from Hinrich Schütze s, linked from http://informationretrieval.org/ IR 26/26: Feature Selection and Exam Overview Paul Ginsparg Cornell University,
More informationChap 2: Classical models for information retrieval
Chap 2: Classical models for information retrieval Jean-Pierre Chevallet & Philippe Mulhem LIG-MRIM Sept 2016 Jean-Pierre Chevallet & Philippe Mulhem Models of IR 1 / 81 Outline Basic IR Models 1 Basic
More informationMATRIX DECOMPOSITION AND LATENT SEMANTIC INDEXING (LSI) Introduction to Information Retrieval CS 150 Donald J. Patterson
MATRIX DECOMPOSITION AND LATENT SEMANTIC INDEXING (LSI) Introduction to Information Retrieval CS 150 Donald J. Patterson Content adapted from Hinrich Schütze http://www.informationretrieval.org Latent
More informationPROBABILISTIC LATENT SEMANTIC ANALYSIS
PROBABILISTIC LATENT SEMANTIC ANALYSIS Lingjia Deng Revised from slides of Shuguang Wang Outline Review of previous notes PCA/SVD HITS Latent Semantic Analysis Probabilistic Latent Semantic Analysis Applications
More informationSemantics with Dense Vectors. Reference: D. Jurafsky and J. Martin, Speech and Language Processing
Semantics with Dense Vectors Reference: D. Jurafsky and J. Martin, Speech and Language Processing 1 Semantics with Dense Vectors We saw how to represent a word as a sparse vector with dimensions corresponding
More informationPV211: Introduction to Information Retrieval
PV211: Introduction to Information Retrieval http://www.fi.muni.cz/~sojka/pv211 IIR 11: Probabilistic Information Retrieval Handout version Petr Sojka, Hinrich Schütze et al. Faculty of Informatics, Masaryk
More informationText Analytics (Text Mining)
http://poloclub.gatech.edu/cse6242 CSE6242 / CX4242: Data & Visual Analytics Text Analytics (Text Mining) Concepts, Algorithms, LSI/SVD Duen Horng (Polo) Chau Assistant Professor Associate Director, MS
More informationEIGENVALE PROBLEMS AND THE SVD. [5.1 TO 5.3 & 7.4]
EIGENVALE PROBLEMS AND THE SVD. [5.1 TO 5.3 & 7.4] Eigenvalue Problems. Introduction Let A an n n real nonsymmetric matrix. The eigenvalue problem: Au = λu λ C : eigenvalue u C n : eigenvector Example:
More informationAn R Package for Latent Semantic Analysis
An R Package for Latent Semantic Analysis LSA-TEL, March 29th 2007, Heerlen, NL Fridolin Wild Vienna University of Economics and Business Administration Structure of the Talk Concepts of the Package Analysis
More informationA Note on the Effect of Term Weighting on Selecting Intrinsic Dimensionality of Data
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 1 Sofia 2009 A Note on the Effect of Term Weighting on Selecting Intrinsic Dimensionality of Data Ch. Aswani Kumar 1,
More informationText Analytics (Text Mining)
http://poloclub.gatech.edu/cse6242 CSE6242 / CX4242: Data & Visual Analytics Text Analytics (Text Mining) Concepts, Algorithms, LSI/SVD Duen Horng (Polo) Chau Assistant Professor Associate Director, MS
More informationMatrix decompositions and latent semantic indexing
18 Matrix decompositions and latent semantic indexing On page 113, we introduced the notion of a term-document matrix: an M N matrix C, each of whose rows represents a term and each of whose columns represents
More informationIR Models: The Probabilistic Model. Lecture 8
IR Models: The Probabilistic Model Lecture 8 ' * ) ( % $ $ +#! "#! '& & Probability of Relevance? ' ', IR is an uncertain process Information need to query Documents to index terms Query terms and index
More informationRanking-II. Temporal Representation and Retrieval Models. Temporal Information Retrieval
Ranking-II Temporal Representation and Retrieval Models Temporal Information Retrieval Ranking in Information Retrieval Ranking documents important for information overload, quickly finding documents which
More informationOutline for today. Information Retrieval. Cosine similarity between query and document. tf-idf weighting
Outline for today Information Retrieval Efficient Scoring and Ranking Recap on ranked retrieval Jörg Tiedemann jorg.tiedemann@lingfil.uu.se Department of Linguistics and Philology Uppsala University Efficient
More informationLet A an n n real nonsymmetric matrix. The eigenvalue problem: λ 1 = 1 with eigenvector u 1 = ( ) λ 2 = 2 with eigenvector u 2 = ( 1
Eigenvalue Problems. Introduction Let A an n n real nonsymmetric matrix. The eigenvalue problem: EIGENVALE PROBLEMS AND THE SVD. [5.1 TO 5.3 & 7.4] Au = λu Example: ( ) 2 0 A = 2 1 λ 1 = 1 with eigenvector
More informationInformation Retrieval. Lecture 6
Information Retrieval Lecture 6 Recap of the last lecture Parametric and field searches Zones in documents Scoring documents: zone weighting Index support for scoring tf idf and vector spaces This lecture
More informationDocument Similarity in Information Retrieval
Document Similarity in Information Retrieval Mausam (Based on slides of W. Arms, Dan Jurafsky, Thomas Hofmann, Ata Kaban, Chris Manning, Melanie Martin) Sec. 1.1 Unstructured data in 1620 Which plays of
More informationDISTRIBUTIONAL SEMANTICS
COMP90042 LECTURE 4 DISTRIBUTIONAL SEMANTICS LEXICAL DATABASES - PROBLEMS Manually constructed Expensive Human annotation can be biased and noisy Language is dynamic New words: slangs, terminology, etc.
More informationNon-Boolean models of retrieval: Agenda
Non-Boolean models of retrieval: Agenda Review of Boolean model and TF/IDF Simple extensions thereof Vector model Language Model-based retrieval Matrix decomposition methods Non-Boolean models of retrieval:
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 18: Latent Semantic Indexing Hinrich Schütze Center for Information and Language Processing, University of Munich 2013-07-10 1/43
More informationEmbeddings Learned By Matrix Factorization
Embeddings Learned By Matrix Factorization Benjamin Roth; Folien von Hinrich Schütze Center for Information and Language Processing, LMU Munich Overview WordSpace limitations LinAlgebra review Input matrix
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Seungjin Choi Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro, Nam-gu, Pohang 37673, Korea seungjin@postech.ac.kr
More informationINF 141 IR METRICS LATENT SEMANTIC ANALYSIS AND INDEXING. Crista Lopes
INF 141 IR METRICS LATENT SEMANTIC ANALYSIS AND INDEXING Crista Lopes Outline Precision and Recall The problem with indexing so far Intuition for solving it Overview of the solution The Math How to measure
More informationRecap of the last lecture. CS276A Information Retrieval. This lecture. Documents as vectors. Intuition. Why turn docs into vectors?
CS276A Information Retrieval Recap of the last lecture Parametric and field searches Zones in documents Scoring documents: zone weighting Index support for scoring tf idf and vector spaces Lecture 7 This
More informationA Neural Passage Model for Ad-hoc Document Retrieval
A Neural Passage Model for Ad-hoc Document Retrieval Qingyao Ai, Brendan O Connor, and W. Bruce Croft College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA,
More informationCS 572: Information Retrieval
CS 572: Information Retrieval Lecture 11: Topic Models Acknowledgments: Some slides were adapted from Chris Manning, and from Thomas Hoffman 1 Plan for next few weeks Project 1: done (submit by Friday).
More informationInformation Retrieval
Introduction to Information Retrieval Lecture 12: Language Models for IR Outline Language models Language Models for IR Discussion What is a language model? We can view a finite state automaton as a deterministic
More informationProblems. Looks for literal term matches. Problems:
Problems Looks for literal term matches erms in queries (esp short ones) don t always capture user s information need well Problems: Synonymy: other words with the same meaning Car and automobile 电脑 vs.
More informationInformation Retrieval Basic IR models. Luca Bondi
Basic IR models Luca Bondi Previously on IR 2 d j q i IRM SC q i, d j IRM D, Q, R q i, d j d j = w 1,j, w 2,j,, w M,j T w i,j = 0 if term t i does not appear in document d j w i,j and w i:1,j assumed to
More informationUniversity of Illinois at Urbana-Champaign. Midterm Examination
University of Illinois at Urbana-Champaign Midterm Examination CS410 Introduction to Text Information Systems Professor ChengXiang Zhai TA: Azadeh Shakery Time: 2:00 3:15pm, Mar. 14, 2007 Place: Room 1105,
More informationMotivation. User. Retrieval Model Result: Query. Document Collection. Information Need. Information Retrieval / Chapter 3: Retrieval Models
3. Retrieval Models Motivation Information Need User Retrieval Model Result: Query 1. 2. 3. Document Collection 2 Agenda 3.1 Boolean Retrieval 3.2 Vector Space Model 3.3 Probabilistic IR 3.4 Statistical
More informationLatent Semantic Analysis (Tutorial)
Latent Semantic Analysis (Tutorial) Alex Thomo Eigenvalues and Eigenvectors Let A be an n n matrix with elements being real numbers. If x is an n-dimensional vector, then the matrix-vector product Ax is
More informationTDDD43. Information Retrieval. Fang Wei-Kleiner. ADIT/IDA Linköping University. Fang Wei-Kleiner ADIT/IDA LiU TDDD43 Information Retrieval 1
TDDD43 Information Retrieval Fang Wei-Kleiner ADIT/IDA Linköping University Fang Wei-Kleiner ADIT/IDA LiU TDDD43 Information Retrieval 1 Outline 1. Introduction 2. Inverted index 3. Ranked Retrieval tf-idf
More informationOn the Foundations of Diverse Information Retrieval. Scott Sanner, Kar Wai Lim, Shengbo Guo, Thore Graepel, Sarvnaz Karimi, Sadegh Kharazmi
On the Foundations of Diverse Information Retrieval Scott Sanner, Kar Wai Lim, Shengbo Guo, Thore Graepel, Sarvnaz Karimi, Sadegh Kharazmi 1 Outline Need for diversity The answer: MMR But what was the
More information3. Basics of Information Retrieval
Text Analysis and Retrieval 3. Basics of Information Retrieval Prof. Bojana Dalbelo Bašić Assoc. Prof. Jan Šnajder With contributions from dr. sc. Goran Glavaš Mladen Karan, mag. ing. University of Zagreb
More informationMaschinelle Sprachverarbeitung
Maschinelle Sprachverarbeitung Retrieval Models and Implementation Ulf Leser Content of this Lecture Information Retrieval Models Boolean Model Vector Space Model Inverted Files Ulf Leser: Maschinelle
More informationPV211: Introduction to Information Retrieval https://www.fi.muni.cz/~sojka/pv211
PV211: Introduction to Information Retrieval https://www.fi.muni.cz/~sojka/pv211 IIR 18: Latent Semantic Indexing Handout version Petr Sojka, Hinrich Schütze et al. Faculty of Informatics, Masaryk University,
More informationFast LSI-based techniques for query expansion in text retrieval systems
Fast LSI-based techniques for query expansion in text retrieval systems L. Laura U. Nanni F. Sarracco Department of Computer and System Science University of Rome La Sapienza 2nd Workshop on Text-based
More informationPV211: Introduction to Information Retrieval
PV211: Introduction to Information Retrieval http://www.fi.muni.cz/~sojka/pv211 IIR 6: Scoring, term weighting, the vector space model Handout version Petr Sojka, Hinrich Schütze et al. Faculty of Informatics,
More information