Browsing by Author "Xu, Tan"
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Item A Context-Aware Approach to Entity Linking(Association for Computational Linguistics, 2012-06-07) Stoyanov, veselin; Mayfield, James; Xu, Tan; Oard, Doug; Lawrie, Dawn; Oates, Tim; Finin, TimEntity linking refers to the task of assigning mentions in documents to their corresponding knowledge base entities. Entity linking is a central step in knowledge base population. Current entity linking systems do not explicitly model the discourse context in which the communication occurs. Nevertheless, the notion of shared context is central to the linguistic theory of pragmatics and plays a crucial role in Grice’s cooperative communication principle. Furthermore, modeling context facilitates joint resolution of entities, an important problem in entity linking yet to be addressed satisfactorily. This paper describes an approach to context-aware entity linking.Item Cross-Document Coreference Resolution: A Key Technology for Learning by Reading(AAAI, 2009-03-23) Mayfield, James; Alexander, David; Dorr, Bonnie; Eisner, Jason; Elsayed, Tamer; Finin, Tim; Fink, Clay; Freedman, Marjorie; Garera, Nikesh; McNamee, Paul; Mohammad, Saif; Oard, Douglas; Piatko, Christine; Sayeed, Asad; Syed, Zareen; Weischedel, Ralph; Xu, Tan; Yarowsky, DavidAutomatic knowledge base population from text is an important technology for a broad range of approaches to learning by reading. Effective automated knowledge base population depends critically upon coreference resolution of entities across sources. Use of a wide range of features, both those that capture evidence for entity merging and those that argue against merging, can significantly improve machine learning-based cross-document coreference resolution. Results from the Global Entity Detection and Recognition task of the NIST Automated Content Extraction (ACE) 2008 evaluation support this conclusion.Item HLTCOE Participation at TAC 2012: Entity Linking and Cold Start Knowledge Base Construction(2012-11) McNamee, Paul; Stoyanov, Veselin; Mayfield, James; Oates, Tim; Finin, Tim; Xu, Tan; Oard, Douglas W.; Lawrie, DawnOur team from the JHU HLTCOE participated in the Entity Linking and Cold Start Knowledge Base tasks in this year’s Text Analysis Conference Knowledge Base Population evaluation. We have previously participated in TAC-KBP entity linking evaluations in 2009, 2010, and 2011. This year we developed two new systems: CALE (Context Aware Linker of Entities) and KELVIN (Knowledge Extraction, Linking, Validation, and INference) to support our research for this year’s exciting tasks.Item KELVIN: a tool for automated knowledge base construction(2013-06-03) McNamee, Paul; Mayfield, James; Finin, Tim; Oates, Tim; Lawrie, Dawn; Xu, Tan; Oard, DougWe present KELVIN, an automated system for processing a large text corpus and distilling a knowledge base about persons, organizations, and locations. We have tested the KELVIN system on several corpora, including: (a) the TAC KBP 2012 Cold Start corpus which consists of public Web pages from the University of Pennsylvania, and (b) a subset of 26k news articles taken from English Gigaword 5th edition. Our NAACL HLT 2013 demonstration permits a user to interact with a set of searchable HTML pages, which are automatically generated from the knowledge base. Each page contains information analogous to the semi-structured details about an entity that are present in Wikipedia Infoboxes, along with hyperlink citations to supporting text.