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Di Lu, Xiaoman Pan, Nima Pourdamghani, Shih-Fu Chang, Heng Ji, Kevin Knight. A Multi-media Approach to Cross-lingual Entity Knowledge Transfer. 2016.

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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


Shih-Fu Chang

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   Author = {Lu, Di and Pan, Xiaoman and Pourdamghani, Nima and Chang, Shih-Fu and Ji, Heng and Knight, Kevin},
   Title = {A Multi-media Approach to Cross-lingual Entity Knowledge Transfer},
   BookTitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics},
   Publisher = {Association for Computational Linguistics},
   Year = {2016}

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