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