Modern Information Retrieval
CLEF '00 Revised Papers from the Workshop of Cross-Language Evaluation Forum on Cross-Language Information Retrieval and Evaluation
Language Resources in Cross-Language Text Retrieval: A CLEF Perspective
CLEF '00 Revised Papers from the Workshop of Cross-Language Evaluation Forum on Cross-Language Information Retrieval and Evaluation
Character N-Gram Tokenization for European Language Text Retrieval
Information Retrieval
Improving Machine Translation Performance by Exploiting Non-Parallel Corpora
Computational Linguistics
Cross-language information retrieval using PARAFAC2
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Translation corpus source and size in bilingual retrieval
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
A semantic feature for statistical machine translation
SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
A vector-space dynamic feature for phrase-based statistical machine translation
Journal of Intelligent Information Systems
Translation techniques in cross-language information retrieval
ACM Computing Surveys (CSUR)
Evaluating indirect strategies for Chinese-Spanish statistical machine translation
Journal of Artificial Intelligence Research
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An area of recent interest in cross-language information retrieval (CLIR) is the question of which parallel corpora might be best suited to tasks in CLIR, or even to what extent parallel corpora can be obtained or are necessary. One proposal, which in our opinion has been somewhat overlooked, is that the Bible holds a unique value as a multilingual corpus, being (among other things) widely available in a broad range of languages and having a high coverage of modern-day vocabulary. In this paper, we test empirically whether this claim is justified through a series of validation tests on various information retrieval tasks. Our results appear to indicate that our methodology may significantly outperform others recently proposed.