Modern Information Retrieval

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Modern Information Retrieval

• Chapter 2 Modeling

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• Can keywords be used to represent a document or a
  query?
• keywords as query and matching as query
  processing cannot generate good results, in
  general
• ranking algorithm, document relevance and IR model

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• Taxonomy of IR models

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• Ad hoc and filtering retrieval
• ad hoc retrieval static document collection,
  queries submitted
• filtering retrieval static queries, document
  streaming
• user profile describes users preference
• keywords, relevance feedback and dynamic keywords
  adjustment

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• Formal characterization of IR models

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• Classic IR
• Index terms
• deciding on the importance of a term is difficult
• consider a terms semantics as well as its
  distribution in all documents
• weights are used to quantify the importance of
  the index terms for describing the document
  contents

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• mutual independence assumption simplifies the
  task of fast ranking computation

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• Boolean model
• index term weights are binary
• query as a Boolean expression
• not, and, or as connectives
• Users might find it difficult to specify their
  information needs

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• advantages and disadvantages
• each document is either relevant or non-relevant
• given (0,1,0), is document dj an answer?

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• Vector model
• Allows partial matching and ranking by a
  similarity measure

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• Computing index term weights
• term frequency, tf factor how well the term
  describes the document contents
• inverse document frequency, idf factor how well
  the term represents the document

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• the vector model is a popular retrieval model due
  to its simplicity and performance