Document (#44047)

Author

Qi, Q.
Hessen, D.J.
Heijden, P.G.M. van der

Title

Improving information retrieval through correspondenceanalysis instead of latent semantic analysis

Source

Journal of intelligent information systems [https://doi.org/10.1007/s10844-023-00815-y]

Year

2023

Abstract

The initial dimensions extracted by latent semantic analysis (LSA) of a document-term matrixhave been shown to mainly display marginal effects, which are irrelevant for informationretrieval. 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 correspon-dence analysis (CA). In this paper, the information retrieval performance of LSA and CA isempirically compared. Moreover, it is explored whether the two weightings also improve theperformance of CA. The results for four empirical datasets show that CA always performsbetter 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 weightingexponent often improves the performance of CA; however, the extent of the improvementdepends on the dataset and the number of dimensions. (PDF) Improving information retrieval through correspondence analysis instead of latent semantic analysis.

Content

Vgl.: https://www.researchgate.net/publication/373808290_Improving_information_retrieval_through_correspondence_analysis_instead_of_latent_semantic_analysis.

Theme

Retrievalalgorithmen

Object

Latent Semantic Analysis

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5. Gao, J.; Zhang, J.: Clustered SVD strategies in latent semantic indexing (2005) 0.29

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