|
Size: 2931
Comment:
|
Size: 3000
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 21: | Line 21: |
The following is the formal definition for IR from MIR p 23. |
|
| Line 57: | Line 59: |
| \begin{numbering} \item $D$ is a set composed of logical views (or representations) for the documents in the collection. |
\begin{enumerate} \item $D$ is a set composed of logical views (or representations) for the (\b documents) in the collection. |
| Line 62: | Line 64: |
| \end{numbering} | \end{enumerate} |
Chapter 1 + Section 2.1 Introduction
attachment:InformationRetreivalProcess.jpg
Information Retrieval Process
- Three Models of Browsing
- Flag
- Structure guided
- Hypertext
Section 2.2 A taxonomy of Information Retrieval Models
Predicting which documents are relevant is usaually dependent on a ranking algorithm.
- The three classic models in information retreival are:
Boolean Model: In the boolean model documents and queries are represented as sets of index terms, thus we say this model is a set theoretic model
Vector Model: In the vector model documents and queries are represented as vectors in a t-dimensional space, thus we say that the model is algebraic.
Probabilistic Model: The framework for modeling document and query representations is based on probability theory, and thus we sat that the model is prababilistic.
Section 2.3 Retrieval: Ad hoc and Filtering
The following is the formal definition for IR from MIR p 23.
\usepackage{amsmath}%
\setcounter{MaxMatrixCols}{30}%
\usepackage{amsfonts}%
\usepackage{amssymb}%
\usepackage{graphicx}
\usepackage{geometry}
\newtheorem{theorem}{Theorem}
\newtheorem{acknowledgement}[theorem]{Acknowledgement}
\newtheorem{algorithm}[theorem]{Algorithm}
\newtheorem{axiom}[theorem]{Axiom}
\newtheorem{case}[theorem]{Case}
\newtheorem{claim}[theorem]{Claim}
\newtheorem{conclusion}[theorem]{Conclusion}
\newtheorem{condition}[theorem]{Condition}
\newtheorem{conjecture}[theorem]{Conjecture}
\newtheorem{corollary}[theorem]{Corollary}
\newtheorem{criterion}[theorem]{Criterion}
\newtheorem{definition}[theorem]{Definition}
\newtheorem{example}[theorem]{Example}
\newtheorem{exercise}[theorem]{Exercise}
\newtheorem{lemma}[theorem]{Lemma}
\newtheorem{notation}[theorem]{Notation}
\newtheorem{problem}[theorem]{Problem}
\newtheorem{proposition}[theorem]{Proposition}
\newtheorem{remark}[theorem]{Remark}
\newtheorem{solution}[theorem]{Solution}
\newtheorem{summary}[theorem]{Summary}
\newenvironment{proof}[1][Proof]{\noindent\textbf{#1.} }{\ \rule{0.5em}{0.5em}}
\geometry{left=0.5in,right=0.5in,top=0.5in,bottom=0.5in}
%%end-prologue%%
\begin{definition}
An information retrieval model is a quadruple $D,Q,F,R(q_i , d_j))$ where
\begin{enumerate}
\item $D$ is a set composed of logical views (or representations) for the (\b documents) in the collection.
\item $Q$ is a set composed of logical views (or representations) for the user information needs. Such representations are called queries
\item $F$ is a framework for modeling document representations, queries and their relationships.
\item $R(q_i,d_j)$ is a ranging function wich associates a real number with a query $q_i \in Q$ and a document represenation $d_j \in D$. Such ranking defines an ordering among the documents with regard to the query $q_i$.
\end{enumerate}
\end{definition}