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Formalization and Visualization of the Narrative for Museum Guides Read chapter Read first chapter

2018 | OriginalPaper | Chapter

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

Authors : Colin Layfield, Dragan Ivanović, Joel Azzopardi

Published in: Semantic Keyword-Based Search on Structured Data Sources

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Wikidata and DBpedia: A Comparative Study
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Footnotes
1
As a side effect, the XML turned out to be badly formed in places and needed to be fixed by hand.
 
3
Diacritics are added to the top or bottom of a letter to indicate appropriate stress, special pronunciation, or unusual sounds not common in the Roman alphabet. In Serbian and Croatian, these markings indicate special pronunciation, like the difference between the pronunciation of C compared to Ć.
 
4
The stop word list is available at http://​www.​lextek.​com/​manuals/​onix/​stopwords1.​html. Note that single character stop words were not included as it was found that many Serbian/Croatian documents were flagged as English when they were present in the list.
 
5
We discovered, serendipitously, that the results of using tf-idf and l-e were actually superior when the folded-in search queries were only weighted using raw term-frequency. This was unexpected and will be a topic of future research. The results reported here use the commonly accepted approach of weighting the query appropriately with the weighting method used for the creation of the semantic space.
 
6
The same similarity score is the cosine similarity between the two ‘mate’ documents.
 
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Metadata
Title
Multi-Lingual LSA with Serbian and Croatian: An Investigative Case Study
Authors
Colin Layfield
Dragan Ivanović
Joel Azzopardi
Copyright Year
2018
Book
Semantic Keyword-Based Search on Structured Data Sources

Print ISBN: 978-3-319-74496-4

Electronic ISBN: 978-3-319-74497-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-74497-1_15