1. Modern Information Retrieval Chapter 2 Modeling

2. 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

3. Taxonomy of IR models

4. 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

5. Formal characterization of IR models

6. 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

7.  mutual independence assumption simplifies the task of fast ranking computation

8. 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

9. advantages and disadvantages  each document is either relevant or non- relevant given = (0,1,0), is document d j an answer?

10. Vector model Allows partial matching and ranking by a similarity measure

12. 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

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