Evaluation of Latent Semantic Analysis in Multilingual Information Retrieval

Abstract

Multilingual Information Retrieval (MLIR) systems have become essential tools in a digitally integrated economy. Users require pertinent information in various languages and across linguistic frontiers. A technique rooted in linear algebra and statistical semantics known as Latent Semantic Analysis (LSA) offers a solution for revealing patterns buried within the data, which may cut across languages. In this paper, we investigate the efficiency of LSA in MLIR tasks with various language pairs compared to traditional vector space models and the machine translation approach. Using the Europarl and CLEF corpora, and employing mean average precision (MAP), precision at 10 (P@10), and normalized Discounted Cumulative Gain (DCG), we demonstrate that LSA facilitates reasonable cross-lingual alignment under specific conditions. Moreover, we assess the model's performance considering changes in the number of latent dimensions and various preprocessing techniques applied before the central processing.