The document discusses different information retrieval models including boolean, vector, and probabilistic models. The boolean model uses set theory and boolean algebra to represent documents and queries. The vector model assigns non-binary weights to terms and ranks documents by similarity to the query. The probabilistic model assigns a probability of relevance to each document given a query.

1. Chapter 2 Modeling

5. 2.2 A Taxonomy of IR Models Set Theoretic Fuzzy Extended Boolean Algebraic Generalized Vector Lat. Semantic Index Neural Networks Probabilistic Inference Network Belief Network U s e r T a s k Retrieval: Ad hoc Filtering Browsing Classic Models boolean vector probabilistic Structured Models Non-Overlapping Lists Proximal Nodes Browsing Flat Structure Guided Hypertext

14. Boolean Model (Cont.) 병렬 프로그램 시스템 1 1 0 … 0 1 1 … 0 0 1 … 1 0 1 … 병렬 프로그램 시스템 … 색인어 1 0 0 1 유사도 004 003 002 001 문서

20. Vector model (Cont.) .176 .176 .477 0 0 .176 .477 .477 .477 .176 0 idf truck shipment silver of in gold fire delivery damaged arrived a Term

21. Vector model (Cont.) Hence, the ranking would be D 2 , D 3 , D 1 Document vectors Not normalized .176 0 .477 0 0 .176 0 0 0 0 0 Q .176 .176 0 0 0 .176 0 0 0 .176 0 D 3 .176 0 .954 0 0 0 0 .477 0 .176 0 D 2 0 .176 0 0 0 .176 .477 0 .477 0 0 D 1 t 11 t 10 t 9 t 8 t 7 t 6 t 5 t 4 t 3 t 2 t 1