Speaker: Muyu Zhang from Harbin Institute of Technology, China
Title: Triple based Background Knowledge Ranking for Document Enrichment
Document enrichment is the task of retrieving additional knowledge from external resource over what is available through source document. This task is essential because of the phenomenon that text is generally replete with gaps and ellipses since authors assume a certain amount of background knowledge. The recovery of these gaps is intuitively useful for better understanding of document.
In this talk, I will introduce my work on this topic which was published on Coling 2014. We propose a document enrichment framework which automatically extracts “argument1; predicate; argument2” triple from any text corpus as background knowledge, so that to ensure the compatibility with any resource (e.g. news text, ontology, and on-line encyclopedia) and improve the enriching accuracy. We first incorporate source document and background knowledge together into a triple based document-level graph and then propose a global iterative ranking model to propagate relevance score and select the most relevant knowledge triple.