Abstract 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.

A WordNet-like linguistic resource is useful, but difficult to construct. This article proposes a method to integrate five linguistic resources, including English/Chinese sense-tagged corpora, English/Chinese thesauruses, and a bilingual dictionary. Chinese words are mapped into WordNet. A Chinese WordNet and a Chinese-English WordNet are derived by following the structures of WordNet. Experiments with Chinese-English information retrieval are developed to evaluate the applicability of the Chinese-English WordNet. The best model achieves 0.1010 average precision, 69.23% of monolingual information retrieval. It also gains a 10.02% increase relative to a model that resolves translation ambiguity and target polysemy problems together.

This paper deals with translation ambiguity and target polysemy problems together.Two monolingual balanced corpora are employed to learn word co-occurrence for translation ambiguity resolution, and augmented translation restrictions for target polysemy resolution.Experiments show that the model achieves 62.92% of monolingual information retrieval, and is 40.80% addition to the select-all model.Combining the target polysemy resolution, the retrieval performance is about 10.11% increase to the model resolving translation ambiguity only.