Beyond English text: Multilingual and multimedia information retrieval.

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Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which needs translation functionality of a relatively low level of sophistication since current models for information retrieval (IR) are still based on a bag-of-words. The Web provides a vast resource for the automatic construction of parallel corpora which can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this paper, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.Comment: 37 page

On Yao's method of translation

Machine Translation, i.e., translating one kind of natural language to another kind of natural language by using a computer system, is a very important research branch in Artificial Intelligence. Yao developed a method of translation that he called ``Lexical-Semantic Driven". In his system he introduced 49 ``relation types" including case relations, event relations, semantic relations, and complex relations. The knowledge graph method is a new kind of method to represent an interlingua between natural languages. In this paper, we will give a comparison of these two methods. We will translate one Chinese sentence cited in Yao�s book by using these two methods. Finally, we will use the relations in knowledge graph theory to represent the ``relations" in Lexical-Semantic Driven, and partition the relations in Lexical-Semantic Driven into groups according to the relations in knowledge graph theory