A Probabilistic Translation Method for Dictionary-based Cross-lingual Information Retrieval in Agglutinative Languages
Abstract
Translation ambiguity, out of vocabulary words and missing some translations in bilingual dictionaries make dictionary-based Cross-language Information Retrieval (CLIR) a challenging task. Moreover, in agglutinative languages which do not have reliable stemmers, missing various lexical formations in bilingual dictionaries degrades CLIR performance. This paper aims to introduce a probabilistic translation model to solve the ambiguity problem, and also to provide most likely formations of a dictionary candidate. We propose Minimum Edit Support Candidates (MESC) method that exploits a monolingual corpus and a bilingual dictionary to translate users' native language queries to documents' language. Our experiments show that the proposed method outperforms state-of-the-art dictionary-based English-Persian CLIR.
Cite
@article{arxiv.1411.1006,
title = {A Probabilistic Translation Method for Dictionary-based Cross-lingual Information Retrieval in Agglutinative Languages},
author = {Javid Dadashkarimi and Azadeh Shakery and Heshaam Faili},
journal= {arXiv preprint arXiv:1411.1006},
year = {2014}
}
Comments
The 3rd conference of Computational Linguistic, Sharif University of Technology, November 2014