A Multi media Approach to Cross lingual Entity Knowledge Transfer
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.
ACL