Benefits of the `Massively Parallel Rosetta Stone': Cross-Language Information Retrieval with over 30 Languages
Peter Chee Tong Chew, Ahmed Abdelalí · Meeting of the Association for Computational Linguistics · 2007
In this paper, we describe our experiences in extending a standard cross-language information retrieval (CLIR) approach which uses parallel aligned corpora and Latent Semantic Indexing. Most, if not all, previous work which follows this approach has focused on bilingual retrieval; two examples involve the use of FrenchEnglish or English-Greek parallel corpora. Our extension to the approach is ‘massively parallel’ in two senses, one linguistic and the other computational. First, we make use of a parallel aligned corpus consisting of almost 50 parallel translations in over 30 distinct languages, each in over 30,000 documents. Given the size of this dataset, a ‘massively parallel’ approach was also necessitated in the more usual computational sense. Our results indicate that, far from adding more noise, more linguistic parallelism is better when it comes to cross-language retrieval precision, in addition to the self-evident benefit that CLIR can be performed on more languages.