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dc.contributor.authorMcParland, Andrew
dc.contributor.authorNewell, Chris
dc.contributor.authorSadrzadeh, Mehrnoosh
dc.date.accessioned2018-11-14T12:17:36Z
dc.date.available2018-11-14T12:17:36Z
dc.date.issued2018-11-14
dc.date.submitted2018-11-14T11:29:53.449Z
dc.identifier.issn2043-0167
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/50523
dc.description.abstractThis note describes a few suggestions for improving entity disambiguation using a vector-based semantic tagger, trained using the Skipgram model. The suggestions include assuming non uniform distributions for the probability distribution of the entity, different ways of building vectors for the document, and using a neural network architecture. We exemplify the suggestions on a running example: the disambiguation of entity Boston, which may be referring to the famous city in Massachusetts US, or a town in Lincolnshire UK, or as we will see in the examples below, a few other places in the US and UK.en_US
dc.relation.ispartofseriesEECSRR–18-01;EECSRR–18-01
dc.subjectentity-document disambiguation, semantic tagging, Skipgram, vector space semanticsen_US
dc.titleImproving entity disambiguation with a vector space semantic taggeren_US
dc.typeTechnical Reporten_US


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