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dc.contributor.authorRenals, Steve
dc.contributor.authorGotoh, Yoshihiko
dc.date.accessioned2006-05-26T16:43:58Z
dc.date.available2006-05-26T16:43:58Z
dc.date.issued1999
dc.identifier.citationIn Proc. ESCA Workshop on Accessing Information In Spoken Audio, pages 43-48, Cambridge, 1999.en
dc.identifier.urihttp://hdl.handle.net/1842/1199
dc.description.abstractIn this paper we describe stochastic finite state model for named entity (NE) identification, based on explicit word-level n-gram relations. NE categories are incorporated in the model as word attributes. We present an overview of the approach, describing how the extensible vocabulary model may be used for NE identification. We report development and evaluation results on a North American Broadcast News task. This approach resulted in average precision and recall scores of around 83% on hand transcribed data, and 73% on the SPRACH recogniser output. We also present an error analysis and a comparison of our approach with an alternative statistical approach.en
dc.format.extent40037 bytes
dc.format.extent67180 bytes
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleStatistical annotation of named entities in spoken audio.en
dc.typeConference Paperen


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