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Journals and Conferences
We explore unsupervised and supervised whole-document approaches to English NEL with naı̈ve and context clustering. Our best system uses unsupervised entity linking and naı̈ve clustering and scores 66.5% B+ F1 score. Our KB clustering score is competitive with the top systems at 65.6%.
We use a supervised whole-document approach to English Entity Linking with simple clustering approaches. The system extends our TAC 2012 system (Radford et al., 2012), introducing new features for modelling local entity description and type-specific matching as well type-specific supervised models and supervised NIL classification. Our rule-based clustering… (More)
We report on a four year academic research project to build a natural language processing platform in support of a large media company. The Computable News platform processes news stories, producing a layer of structured data that can be used to build rich applications. We describe the underlying platform and the research tasks that we explored building it.… (More)