Cross lingual entity linking means linking an entity mention in a background source document in one language with the corresponding real world entity in a knowledge base written in the other language. The key problem is to measure the similarity score between the context of the entity mention and the document of the candidate entity. This paper presents a general framework for doing cross lingual entity linking by leveraging a large scale and bilingual knowledge base, Wikipedia. We introduce a bilingual topic model that mining bilingual topic from this knowledge base with the assumption that the same Wikipedia concept documents of two different languages share the same semantic topic distribution. The extracted topics have two types of representation, with each type corresponding to one language. Thus both the context of the entity mention and the document of the candidate entity can be represented in a space using the same semantic topics. We use these topics to do cross lingual entity linking. Experimental results show that the proposed approach can obtain the competitive results compared with the state-of-art approach.

This paper is concerned with data selection for adapting language model (LM) in statistical machine translation (SMT), and aims to find the LM training sentences that are topic similar to the translation task. Although the traditional approaches have gained significant performance, they ignore the topic information and the distribution information of words when selecting similar training sentences. In this paper, we present two bilingual topic model (BLTM) (joint and coupled BLTM) based sentence representations for cross-lingual data selection. We map the data selection task into cross-lingual semantic representations that are language independent, then rank and select sentences in the target language LM training corpus for a sentence in the translation task by the semantics-based likelihood. The semantic representations are learned from the parallel corpus, with the assumption that the bilingual pair shares the same or similar distribution over semantic topics. Large-scale experimental results demonstrate that our approaches significantly outperform the state-of-the-art approaches on both LM perplexity and translation performance, respectively.