Translating–transliterating named entities for multilingual information access

Named entities are major constituents of a document but are usually unknown words. This work proposes a systematic way of dealing with formulation, transformation, translation, and transliteration of multilingual‐named entities. The rules and similarity matrices for translation and transliteration are learned automatically from parallel‐named‐entity corpora. The results are applied in cross‐language access to collections of images with captions. Experimental results demonstrate that the similarity‐based transliteration of named entities is effective, and runs in which transliteration is considered outperform the runs in which it is neglected.