Modern Information Retrieval

Chapter 2 Modeling

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

Taxonomy of IR models

Ad hoc and filtering retrieval ad hoc retrieval: static document

collection, queries submitted filtering retrieval: static queries,

document streaming user profile describes user’s preference keywords, relevance feedback and

dynamic keywords adjustment

Formal characterization of IR models

Classic IR Index terms

deciding on the importance of a term is difficult

consider a term’s semantics as well as its distribution in all documents

weight’s are used to quantify the importance of the index terms for describing the document contents

mutual independence assumption simplifies the task of fast ranking computation

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

advantages and disadvantages each document is either relevant or non-

relevant given = (0,1,0), is document dj an answer?

Vector model Allows partial matching and ranking

by a similarity measure

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

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