- •List of Tables
- •List of Figures
- •Table of Notation
- •Preface
- •Boolean retrieval
- •An example information retrieval problem
- •Processing Boolean queries
- •The extended Boolean model versus ranked retrieval
- •References and further reading
- •The term vocabulary and postings lists
- •Document delineation and character sequence decoding
- •Obtaining the character sequence in a document
- •Choosing a document unit
- •Determining the vocabulary of terms
- •Tokenization
- •Dropping common terms: stop words
- •Normalization (equivalence classing of terms)
- •Stemming and lemmatization
- •Faster postings list intersection via skip pointers
- •Positional postings and phrase queries
- •Biword indexes
- •Positional indexes
- •Combination schemes
- •References and further reading
- •Dictionaries and tolerant retrieval
- •Search structures for dictionaries
- •Wildcard queries
- •General wildcard queries
- •Spelling correction
- •Implementing spelling correction
- •Forms of spelling correction
- •Edit distance
- •Context sensitive spelling correction
- •Phonetic correction
- •References and further reading
- •Index construction
- •Hardware basics
- •Blocked sort-based indexing
- •Single-pass in-memory indexing
- •Distributed indexing
- •Dynamic indexing
- •Other types of indexes
- •References and further reading
- •Index compression
- •Statistical properties of terms in information retrieval
- •Dictionary compression
- •Dictionary as a string
- •Blocked storage
- •Variable byte codes
- •References and further reading
- •Scoring, term weighting and the vector space model
- •Parametric and zone indexes
- •Weighted zone scoring
- •Learning weights
- •The optimal weight g
- •Term frequency and weighting
- •Inverse document frequency
- •The vector space model for scoring
- •Dot products
- •Queries as vectors
- •Computing vector scores
- •Sublinear tf scaling
- •Maximum tf normalization
- •Document and query weighting schemes
- •Pivoted normalized document length
- •References and further reading
- •Computing scores in a complete search system
- •Index elimination
- •Champion lists
- •Static quality scores and ordering
- •Impact ordering
- •Cluster pruning
- •Components of an information retrieval system
- •Tiered indexes
- •Designing parsing and scoring functions
- •Putting it all together
- •Vector space scoring and query operator interaction
- •References and further reading
- •Evaluation in information retrieval
- •Information retrieval system evaluation
- •Standard test collections
- •Evaluation of unranked retrieval sets
- •Evaluation of ranked retrieval results
- •Assessing relevance
- •A broader perspective: System quality and user utility
- •System issues
- •User utility
- •Results snippets
- •References and further reading
- •Relevance feedback and query expansion
- •Relevance feedback and pseudo relevance feedback
- •The Rocchio algorithm for relevance feedback
- •Probabilistic relevance feedback
- •When does relevance feedback work?
- •Relevance feedback on the web
- •Evaluation of relevance feedback strategies
- •Pseudo relevance feedback
- •Indirect relevance feedback
- •Summary
- •Global methods for query reformulation
- •Vocabulary tools for query reformulation
- •Query expansion
- •Automatic thesaurus generation
- •References and further reading
- •XML retrieval
- •Basic XML concepts
- •Challenges in XML retrieval
- •A vector space model for XML retrieval
- •Evaluation of XML retrieval
- •References and further reading
- •Exercises
- •Probabilistic information retrieval
- •Review of basic probability theory
- •The Probability Ranking Principle
- •The 1/0 loss case
- •The PRP with retrieval costs
- •The Binary Independence Model
- •Deriving a ranking function for query terms
- •Probability estimates in theory
- •Probability estimates in practice
- •Probabilistic approaches to relevance feedback
- •An appraisal and some extensions
- •An appraisal of probabilistic models
- •Bayesian network approaches to IR
- •References and further reading
- •Language models for information retrieval
- •Language models
- •Finite automata and language models
- •Types of language models
- •Multinomial distributions over words
- •The query likelihood model
- •Using query likelihood language models in IR
- •Estimating the query generation probability
- •Language modeling versus other approaches in IR
- •Extended language modeling approaches
- •References and further reading
- •Relation to multinomial unigram language model
- •The Bernoulli model
- •Properties of Naive Bayes
- •A variant of the multinomial model
- •Feature selection
- •Mutual information
- •Comparison of feature selection methods
- •References and further reading
- •Document representations and measures of relatedness in vector spaces
- •k nearest neighbor
- •Time complexity and optimality of kNN
- •The bias-variance tradeoff
- •References and further reading
- •Exercises
- •Support vector machines and machine learning on documents
- •Support vector machines: The linearly separable case
- •Extensions to the SVM model
- •Multiclass SVMs
- •Nonlinear SVMs
- •Experimental results
- •Machine learning methods in ad hoc information retrieval
- •Result ranking by machine learning
- •References and further reading
- •Flat clustering
- •Clustering in information retrieval
- •Problem statement
- •Evaluation of clustering
- •Cluster cardinality in K-means
- •Model-based clustering
- •References and further reading
- •Exercises
- •Hierarchical clustering
- •Hierarchical agglomerative clustering
- •Time complexity of HAC
- •Group-average agglomerative clustering
- •Centroid clustering
- •Optimality of HAC
- •Divisive clustering
- •Cluster labeling
- •Implementation notes
- •References and further reading
- •Exercises
- •Matrix decompositions and latent semantic indexing
- •Linear algebra review
- •Matrix decompositions
- •Term-document matrices and singular value decompositions
- •Low-rank approximations
- •Latent semantic indexing
- •References and further reading
- •Web search basics
- •Background and history
- •Web characteristics
- •The web graph
- •Spam
- •Advertising as the economic model
- •The search user experience
- •User query needs
- •Index size and estimation
- •Near-duplicates and shingling
- •References and further reading
- •Web crawling and indexes
- •Overview
- •Crawling
- •Crawler architecture
- •DNS resolution
- •The URL frontier
- •Distributing indexes
- •Connectivity servers
- •References and further reading
- •Link analysis
- •The Web as a graph
- •Anchor text and the web graph
- •PageRank
- •Markov chains
- •The PageRank computation
- •Hubs and Authorities
- •Choosing the subset of the Web
- •References and further reading
- •Bibliography
- •Author Index
DRAFT! © April 1, 2009 Cambridge University Press. Feedback welcome. |
xxvii |
Table of Notation
Symbol |
Page |
Meaning |
γ |
p. 98 |
γ code |
γp. 256 Classification or clustering function: γ(d) is d’s class
or cluster
p. 256 Supervised learning method in Chapters 13 and 14:
(D) is the classification function γ learned from training set D
λp. 404 Eigenvalue
~µ(.) |
p. 292 Centroid of a class (in Rocchio classification) or a |
|
cluster (in K-means and centroid clustering) |
Φ
σ
Θ(·)
ω, ωk
Ω
arg maxx f (x) arg minx f (x) c, cj
cft
p. 114 Training example p. 408 Singular value
p. 11 A tight bound on the complexity of an algorithm p. 357 Cluster in clustering
p. 357 Clustering or set of clusters {ω1, . . . , ωK}
p. 181 The value of x for which f reaches its maximum p. 181 The value of x for which f reaches its minimum p. 256 Class or category in classification
p.89 The collection frequency of term t (the total number of times the term appears in the document collection)
Cp. 256 Set {c1, . . . , cJ} of all classes
Cp. 268 A random variable that takes as values members of
C
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Table of Notation |
Cp. 403 Term-document matrix
dp. 4 Index of the dth document in the collection D
dp. 71 A document
~ |
p. 181 Document vector, query vector |
d,~q |
Dp. 354 Set {d1, . . . , dN } of all documents
Dc |
p. 292 Set of documents that is in class c |
Dp. 256 Set {hd1, c1i, . . . , hdN, cNi} of all labeled documents
|
in Chapters 13–15 |
dft |
p. 118 The document frequency of term t (the total number |
|
of documents in the collection the term appears in) |
Hp. 99 Entropy
HM |
p. 101 |
Mth harmonic number |
I(X; Y) |
p. 272 |
Mutual information of random variables X and Y |
idft |
p. 118 |
Inverse document frequency of term t |
Jp. 256 Number of classes
kp. 290 Top k items from a set, e.g., k nearest neighbors in
kNN, top k retrieved documents, top k selected features from the vocabulary V
kp. 54 Sequence of k characters
Kp. 354 Number of clusters
Ld |
p. 233 |
Length of document d (in tokens) |
La |
p. 262 |
Length of the test document (or application docu- |
|
|
ment) in tokens |
Lave |
p. 70 |
Average length of a document (in tokens) |
Mp. 5 Size of the vocabulary (|V|)
Ma |
p. 262 |
Size of the vocabulary of the test document (or ap- |
|
|
plication document) |
Mave |
p. 78 |
Average size of the vocabulary in a document in the |
|
|
collection |
Md |
p. 237 |
Language model for document d |
Np. 4 Number of documents in the retrieval or training
|
|
collection |
Nc |
p. 259 |
Number of documents in class c |
N(ω) |
p. 298 |
Number of times the event ω occurred |
Online edition (c) 2009 Cambridge UP
Table of Notation
O(·)
O(·)
P
P(·)
P
q
R
si si
sim(d1, d2)
T
Tct
t
t tft,d
Ut
xxix
p. 11 A bound on the complexity of an algorithm p. 221 The odds of an event
p. 155 Precision p. 220 Probability
p. 465 Transition probability matrix p. 59 A query
p. 155 Recall p. 58 A string
p. 112 Boolean values for zone scoring
p. 121 Similarity score for documents d1, d2
p. 43 Total number of tokens in the document collection
p.259 Number of occurrences of word t in documents of class c
p. 4 Index of the tth term in the vocabulary V p. 61 A term in the vocabulary
p.117 The term frequency of term t in document d (the total number of occurrences of t in d)
p.266 Random variable taking values 0 (term t is present) and 1 (t is not present)
Vp. 208 Vocabulary of terms {t1, . . . , tM} in a collection (a.k.a.
|
|
the lexicon) |
~v(d) |
p. 122 |
Length-normalized document vector |
~ |
p. 120 |
Vector of document d, not length-normalized |
V(d) |
||
wft,d |
p. 125 |
Weight of term t in document d |
wp. 112 A weight, for example for zones or terms
w~ T~x = b |
p. 293 |
Hyperplane; w~ is the normal vector of the hyper- |
|
|
plane and wi component i of w~ |
~x |
p. 222 |
Term incidence vector ~x = (x1, . . . , xM); more gen- |
|
|
erally: document feature representation |
Xp. 266 Random variable taking values in V, the vocabulary
(e.g., at a given position k in a document)
Xp. 256 Document space in text classification
|A| |
p. 61 |
Set cardinality: the number of members of set A |
|S| |
p. 404 |
Determinant of the square matrix S |
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|
Table of Notation |
|si| |
p. 58 |
Length in characters of string si |
|~x| |
p. 139 |
Length of vector ~x |
|~x −~y| |
p. 131 |
Euclidean distance of ~x and ~y (which is the length of |
|
|
(~x −~y)) |
Online edition (c) 2009 Cambridge UP