2016Engineering and Technology JournalOpen access

Enhance Inverted Index Using in Information Retrieval

Alia Karim Abdul Hassan, Duaa Enteesha mhawi

Open full text 5 citations

Abstract

This paper proposes a method to represent the first step in information retrieval (IR) (that prepare the document set (preprocessing), In Information retrieval systems, tokenization is an integral part whose prime objective is to identify the token and their count. In this paper, an effective tokenization approach which is based on proposed new method called enhance inverted index (EII). The result shows that efficiency/ effectiveness of the proposed algorithm. Tokenization on documents helps to satisfy user’s information need more precisely and reduced search sharply, believed to be a part of information retrieval. Pre-processing of input document is an integral part of Tokenization, which involves preprocessing of documents and generates its respective tokens, which is the basis of these tokens. Probabilistic IR generates its scoring and gives reduced search space. The comparative analysis based on the two parameters; reduce the time of search space, Pre-processing time

Open-access reader

About this research paper

What this paper is about

This paper proposes a method to represent the first step in information retrieval (IR) (that prepare the document set (preprocessing), In Information retrieval systems, tokenization is an integral part whose prime objective is to identify the token and their count. In this paper, an effective tokenization approach which is based on proposed new method called enhance inverted index (EII). The result shows that efficiency/ effectiveness of the proposed algorithm. Tokenization on documents helps to satisfy user’s information need more precisely and reduced search sharply, believed to be a part of information retrieval. Pre-processing of input document is an integral part of Tokenization, which involves preprocessing of documents and generates its respective tokens, which is the basis of these tokens. Probabilistic IR generates its scoring and gives reduced search space. The comparative analysis based on the two parameters; reduce the time of search space, Pre-processing time

Why it matters

OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

This paper proposes a method to represent the first step in information retrieval (IR) (that prepare the document set (preprocessing), In Information retrieval systems, tokenization is an integral part whose prime objective is to identify the token and their count. In this paper, an effective tokenization approach which is based on proposed new method called enhance inverted index (EII). The result shows that efficiency/ effectiveness of the proposed algorithm. Tokenization on documents helps to satisfy user’s information need more precisely and reduced search sharply, believed to be a part of information retrieval. Pre-processing of input document is an integral part of Tokenization, which involves preprocessing of documents and generates its respective tokens, which is the basis of these tokens. Probabilistic IR generates its scoring and gives reduced search space. The comparative analysis based on the two parameters; reduce the time of search space, Pre-processing time

Key concepts: Lexical analysis, Inverted index, Computer science, Security token, Preprocessor, Set (abstract data type), Information retrieval, Index (typography)

Related papers

Back to paper searchBrowse research topicsOriginal source
Enhance Inverted Index Using in Information Retrieval — Research Paper | ScholarLens