- •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. |
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6 Scoring, term weighting and the vector space model
Thus far we have dealt with indexes that support Boolean queries: a document either matches or does not match a query. In the case of large document collections, the resulting number of matching documents can far exceed the number a human user could possibly sift through. Accordingly, it is essential for a search engine to rank-order the documents matching a query. To do this, the search engine computes, for each matching document, a score with respect to the query at hand. In this chapter we initiate the study of assigning a score to a (query, document) pair. This chapter consists of three main ideas.
1.We introduce parametric and zone indexes in Section 6.1, which serve two purposes. First, they allow us to index and retrieve documents by metadata such as the language in which a document is written. Second, they give us a simple means for scoring (and thereby ranking) documents in response to a query.
2.Next, in Section 6.2 we develop the idea of weighting the importance of a term in a document, based on the statistics of occurrence of the term.
3.In Section 6.3 we show that by viewing each document as a vector of such weights, we can compute a score between a query and each document. This view is known as vector space scoring.
Section 6.4 develops several variants of term-weighting for the vector space model. Chapter 7 develops computational aspects of vector space scoring, and related topics.
As we develop these ideas, the notion of a query will assume multiple nuances. In Section 6.1 we consider queries in which specific query terms occur in specified regions of a matching document. Beginning Section 6.2 we will in fact relax the requirement of matching specific regions of a document; instead, we will look at so-called free text queries that simply consist of query terms with no specification on their relative order, importance or where in a document they should be found. The bulk of our study of scoring will be in this latter notion of a query being such a set of terms.
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