Multi-Lingual LSA with Serbian and Croatian: An Investigative Case Study

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10546)


One of the challenges in information retrieval is attempting to search a corpus of documents that may contain multiple languages. This exploratory study expands upon earlier research employing Latent Semantic Analysis (so called Multi-Lingual Latent Semantic Indexing, or ML-LSI/LSA). We experiment using this approach, and a new one, in a multi-lingual context utilising two similar languages, namely Serbian and Croatian. Traditionally, with an LSA approach, a parallel corpus would be needed in order to train the system by combining identical documents in two languages into one document. We repeat that approach and also experiment with creating a semantic space using the parallel corpus on its own without merging the documents together to test the hypothesis that, with very similar languages, the merging of documents may not be required for good results.


Latent Semantic Analysis (LSA) Parallel Corpus Semantic Space Multilingual Information Retrieval Multi-lingual IR 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This article is based upon work from COST Action KEYSTONE IC1302, supported by COST (European Cooperation in Science and Technology).


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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of MaltaMsidaMalta
  2. 2.University of Novi SadNovi SadSerbia

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