Answered: Explain how document (information…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Explain how document (information retrieval ) and text can be retrieved.

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Step 1: Document and text retrieval are fundamental processes in information retrieval systems .

Document Indexing:
- Before retrieval can occur, a collection of documents or text needs to be indexed. Indexing involves analyzing each document and extracting important information, such as words, phrases, and their positions within the document.
- Common techniques include tokenization (splitting text into words or terms), stemming (reducing words to their root form), and removing stopwords (common words like "the" or "and" that don't carry much meaning).
Query Representation:
- When a user submits a query, it needs to be represented in a format that can be compared to the indexed documents. This typically involves the same preprocessing steps used during indexing, such as tokenization and stemming.
- Queries can be represented as velocity in a high-dimensional space, where each dimension corresponds to a term, and the value in each dimension represents the term's importance in the query.
Retrieval Models:
- Information retrieval systems use various models to rank documents based on their relevance to the query. Some common retrieval models include:
  - Boolean Model: Retrieves documents that match specific query terms (AND, OR, NOT operations).

Velocity Space Model (VSM): Represents documents and queries as velocity in a multi-dimensional space and calculates similarity scores (e.g., cosine similarity) to rank documents.

- - Probabilistic Model: Estimates the probability that a document is relevant to a query.
  - BM25: A popular probabilistic ranking function that considers term frequency, document length, and query term saturation.
Scoring and Ranking:
- After representing the query and documents in a common format, the system calculates a relevance score for each document with respect to the query.
- Documents are ranked based on their relevance scores, with the most relevant documents typically displayed first.

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