Browsing by Subject "Knowledge Base Population"
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Item Hot Stuff at Cold Start: HLTCOE participation at TAC 2014(National Institute of Standards and Technology, 2014-10-31) Finin, Tim; Lawrie, Dawn; Mayfield, James; McNamee, Paul; Harman, CraigThe JHU HLTCOE participated in the Cold Start task in this year’s Text Analysis Conference Knowledge Base Population evaluation. This is our third year of participation in the task, and we continued our research with the KELVIN system. We submitted experimental variants that explore use of forward-chaining inference, slightly more aggressive entity clustering, refined multiple within-document conference, and prioritization of relations extracted from news sources.Item KELVIN: Extracting Knowledge from Large Text Collections(AAAI Press, 2014-11-13) Mayfield, James; McNamee, Paul; Harmon, Craig; Finin, Tim; Lawrie, DawnWe describe the KELVIN system for extracting entities and relations from large text collections and its use in the TAC Knowledge Base Population Cold Start task run by the U.S. National Institute of Standards and Technology. The Cold Start task starts with an empty knowledge base defined by an ontology or entity types, properties and relations. Evaluations in 2012 and 2013 were done using a collection of text from local Web and news to de-emphasize the linking entities to a background knowledge bases such as Wikipedia. Interesting features of KELVIN include a cross-document entity coreference module based on entity mentions, removal of suspect intra-document conference chains, a slot value consolidator for entities, the application of inference rules to expand the number of asserted facts and a set of analysis and browsing tools supporting development.