Diff for "unl/Csce810Chapter2"

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.

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\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 {\bf documents} in the collection.
\item $Q$ is a set composed of logical views (or representations) for the user information needs. Such representations are called {\bf queries}
\item $F$ is a {\bf framework} for modeling document representations, queries and their relationships.
\item $R(q_i,d_j)$ is a {\bf ranking 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}

Section 2.5.1 Basic Concepts of Classic IR

• Each document is described by a set of representative key workds calle index terms.

• Index terms are usually nouns. Why? Because verbs, adjectives connectives etc. have little meaning on their own.
• Index terms have weights described as follows:

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\begin{definition}
Let $t$ be the number of index terms in the system and $k_i$ be a generic index term. $K={k_1,...,k_t}$ is the set of all index terms. A weight $w_{i,j} > 0$ is associated with each index term $k_i$ of  a document $d_j$. For an index term which does not appear in the document text, $w_{i,j}=0$. With document $d_j$ is associated an index term vector $\vec{d}_{j}=(w_{1,j},w_{2,j},...,w_{t,j})$. Further, let $g_{i}$ be a function that returns the wieght associated with the index term $k_{i}$ in any $t$-dimensional vector (i.e., $g_{i}(\vec{d}_{j})=w_{i,j}$).
\end{definition}

Section 2.5.2 Boolean Model