Improving information retrieval through correspondence analysis instead of latent semantic analysis

IF 2.3 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Intelligent Information Systems Pub Date : 2023-09-09 DOI:10.1007/s10844-023-00815-y

Qianqian Qi, David J. Hessen, Peter G. M. van der Heijden

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Abstract

Abstract The initial dimensions extracted by latent semantic analysis (LSA) of a document-term matrix have been shown to mainly display marginal effects, which are irrelevant for information retrieval. To improve the performance of LSA, usually the elements of the raw document-term matrix are weighted and the weighting exponent of singular values can be adjusted. An alternative information retrieval technique that ignores the marginal effects is correspondence analysis (CA). In this paper, the information retrieval performance of LSA and CA is empirically compared. Moreover, it is explored whether the two weightings also improve the performance of CA. The results for four empirical datasets show that CA always performs better than LSA. Weighting the elements of the raw data matrix can improve CA; however, it is data dependent and the improvement is small. Adjusting the singular value weighting exponent often improves the performance of CA; however, the extent of the improvement depends on the dataset and the number of dimensions.

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利用对应分析代替潜在语义分析改进信息检索

摘要文献术语矩阵的潜在语义分析(LSA)提取的初始维数主要表现为边际效应，与信息检索无关。为了提高LSA的性能，通常对原始文档项矩阵的元素进行加权，并且可以调整奇异值的加权指数。另一种忽略边际效应的信息检索技术是对应分析(CA)。本文对LSA和CA的信息检索性能进行了实证比较。此外，还探讨了两种权重是否也能提高CA的性能。四个经验数据集的结果表明，CA的性能始终优于LSA。对原始数据矩阵的元素进行加权可以改善CA;然而，它依赖于数据，而且改进很小。调整奇异值加权指数往往能提高CA的性能;然而，改进的程度取决于数据集和维度的数量。

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来源期刊

Journal of Intelligent Information Systems 工程技术-计算机：人工智能

CiteScore

7.20

自引率

11.80%

发文量

审稿时长

6-12 weeks

期刊介绍： The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.