Mining Named Entity Translation from Non Parallel Corpora

In this paper, we address the problem of mining named entity translation such as names of persons, organizations, and locations, from non parallel corpora. First, our study concentrates of different forms of named entity translation. Then, we introduce a new framework to extract all named entity translation types from a non parallel corpus. The proposed framework combines surface and linguistic-based approaches. It is language independent and do not rely on any external parallel resources such as bilingual lexicons or parallel corpora. Evaluations show that our approach for mining named entity translations from a non parallel corpus is highly effective and consistently improves the translation quality of Arabic to French machine translation system.