tailieunhanh - Báo cáo khoa học: "Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models "

Cross-document coreference, the task of grouping all the mentions of each entity in a document collection, arises in information extraction and automated knowledge base construction. For large collections, it is clearly impractical to consider all possible groupings of mentions into distinct entities. To solve the problem we propose two ideas: (a) a distributed inference technique that uses parallelism to enable large scale processing, and (b) a hierarchical model of coreference that represents uncertainty over multiple granularities of entities to facilitate more effective approximate inference. . | Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models Sameer Singh Amarnag Subramanya Fernando Pereira Andrew McCallum Department of Computer Science University of Massachusetts Amherst MA 01002 t Google Research Mountain View CA 94043 sameer@ asubram@ pereira@ mccallum@ Abstract Cross-document coreference the task of grouping all the mentions of each entity in a document collection arises in information extraction and automated knowledge base construction. For large collections it is clearly impractical to consider all possible groupings of mentions into distinct entities. To solve the problem we propose two ideas a a distributed inference technique that uses parallelism to enable large scale processing and b a hierarchical model of coreference that represents uncertainty over multiple granularities of entities to facilitate more effective approximate inference. To evaluate these ideas we constructed a labeled corpus of million disambiguated mentions in Web pages by selecting link anchors referring to Wikipedia entities. We show that the combination of the hierarchical model with distributed inference quickly obtains high accuracy with error reduction of 38 on this large dataset demonstrating the scalability of our approach. 1 Introduction Given a collection of mentions of entities extracted from a body of text coreference or entity resolution consists of clustering the mentions such that two mentions belong to the same cluster if and only if they refer to the same entity. Solutions to this problem are important in semantic analysis and knowledge discovery tasks Blume 2005 Mayfield et al. 2009 . While significant progress has been made in within-document coreference Ng 2005 Culotta et al. 2007 Haghighi and Klein 2007 Bengston and Roth 2008 Haghighi and Klein 793 2009 Haghighi and Klein 2010 the larger problem of cross-document coreference has not received as much attention. Unlike

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