%0 Journal Article %F lumulti %A Lu, Di %A Pan, Xiaoman %A Pourdamghani, Nima %A Chang, Shih-Fu %A Ji, Heng %A Knight, Kevin %T A Multi-media Approach to Cross-lingual Entity Knowledge Transfer %B Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics %I Association for Computational Linguistics %X When a large-scale incident or disaster occurs, there is often a great demand for rapidly developing a system to extract detailed and new information from lowresource languages (LLs). We propose a novel approach to discover comparable documents in high-resource languages (HLs), and project Entity Discovery and Linking results from HLs documents back to LLs. We leverage a wide variety of language-independent forms from multiple data modalities, including image processing (image-to-image retrieval, visual similarity and face recognition) and sound matching. We also propose novel methods to learn entity priors from a large-scale HL corpus and knowledge base. Using Hausa and Chinese as the LLs and English as the HL, experiments show that our approach achieves 36.1% higher Hausa name tagging F-score over a costly supervised model, and 9.4% higher Chineseto-English Entity Linking accuracy over state-of-the-art %U http://www.ee.columbia.edu/ln/dvmm/publications/16/imagetransfer2016.pdf %D 2016