A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Character N-Gram Tokenization for European Language Text Retrieval
Information Retrieval
Char_align: a program for aligning parallel texts at the character level
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Letter level learning for language independent diacritics restoration
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Benefits of resource-based stemming in hungarian information retrieval
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
JHU/APL ad hoc experiments at CLEF 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Cross-lingual random indexing for information retrieval
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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JHU/APL has long espoused the use of language-neutral methods for cross-language information retrieval. This year we participated in the ad hoc cross-language track and submitted both monolingual and bilingual runs. We undertook our first investigations in the Bulgarian and Hungarian languages. In our bilingual experiments we used several non-traditional CLEF query languages such as Greek, Hungarian, and Indonesian, in addition to several western European languages. We found that character n-grams remain an attractive option for representing documents and queries in these new languages. In our monolingual tests n-grams were more effective than unnormalized words for retrieval in Bulgarian (+30%) and Hungarian (+63%). Our bilingual runs made use of subword translation, statistical translation of character n-grams using aligned corpora, when parallel data were available, and web-based machine translation, when no suitable data could be found.