# A Probabilistic Translation Method for Dictionary-based Cross-lingual Information Retrieval in Agglutinative Languages

arXiv: https://arxiv.org/abs/1411.1006 · HTML: https://mlsanity.com/paper/1411.1006/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2014.

## Built on by (most cited first, 1 of 1)

- [What Drives Cross-lingual Ranking? Retrieval Approaches with Multilingual Language Models](https://mlsanity.com/paper/2511.19324/) (2025)

## Abstract (arXiv)

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.