Latent Semantic Indexing (LSI) is a famous Information Retrieval (IR) technique that tries to overcome the problems of lexical matching using conceptual indexing. LSI is a variant of vector space model and proved to be 30% more effective. Many studies have reported that good retrieval performance is related to the use of various retrieval heuristics. In this paper, we focus on optimising two LSI retrieval heuristics: term weighting and rank approximation. The results obtained demonstrate that the LSI performance improves significantly with the combination of optimised term weighting and rank approximation.

Srinivas Koppu

Department of Smart Computing

School of Information Technology and Engineering

Vellore Campus

Aswani Kumar Cherukuri

Department of Information Technology

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Optimising the Heuristics in Latent Semantic Indexing for Effective Information Retrieval

Journal of Information & Knowledge Management

While reducing the dimensionality of a corpus, concept decomposition (CD) based on fuzzy K-means (FKM) clustering provides better approximation than CD based on spherical k-means clustering. However, performance of the FKM algorithm is limited by its distance metric and it is proved that assignment of feature weights can improve the performance of FKM. Our work builds upon this analysis and proposes two approaches to feature weight selection. Using four testing document collections, we demonstrate that the CD based on the proposed feature-weighted FKM provides better approximation than the CD based on FKM while maintaining the quality of retrieval. Copyright © 2010 Taylor &amp; Francis Group, LLC.

Cybernetics and Systems

A NOTE ON WEIGHTED FUZZY K-MEANS CLUSTERING FOR CONCEPT DECOMPOSITION

The primary goal of an information retrieval system is to retrieve all the documents that are relevant to the user query. Disparities between the vocabulary of the system's authors and that of their users pose difficulties when information is processed without human intervention. Preprocessing the documents and user queries using intelligence techniques to remove the ambiguities in representation and indexing is the current area of research. In this paper, we present a novel intelligent method that has been appended to existing stemming and stopword removal processes. We designed an information retrieval system based on the proposed method using latent semantic indexing. The experimental results of the system using the proposed method exhibits the superiority over other systems based on traditional preprocessing methods.

An Information Retrieval Model Based on Latent Semantic Indexing with Intelligent Preprocessing

Information Processing & Management

A framework for understanding Latent Semantic Indexing (LSI) performance

Term norm distribution and its effects on Latent Semantic Indexing

Information Retrieval

Parallel Information Retrieval

Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR '04

A formal study of information retrieval heuristics

Journal	Journal of Information & Knowledge Management
Publisher	World Scientific Pub Co Pte Lt
ISSN	0219-6492
Open Access	0