A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping

dc.contributor.authorBloodgood, Michael
dc.contributor.authorVijay-Shanker, K
dc.date.accessioned2014-09-09T00:04:09Z
dc.date.available2014-09-09T00:04:09Z
dc.date.issued2009-06
dc.description.abstractA survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermore, stopping methods are required to handle a broad range of different annotation/performance tradeoff valuations. Despite this, the existing body of work is dominated by conservative methods with little (if any) attention paid to providing users with control over the behavior of stopping methods. The proposed method is shown to fill a gap in the level of aggressiveness available for stopping AL and supports providing users with control over stopping behavior.en_US
dc.identifierhttps://doi.org/10.13016/M25P4M
dc.identifier.citationMichael Bloodgood and K. Vijay-Shanker. 2009. A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009), pages 39-47, Boulder, Colorado, June. Association for Computational Linguistics.en_US
dc.identifier.urihttp://hdl.handle.net/1903/15596
dc.language.isoen_USen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.relation.isAvailableAtCenter for Advanced Study of Language
dc.relation.isAvailableAtDigitial Repository at the University of Maryland
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md)
dc.subjectcomputer scienceen_US
dc.subjectstatistical methodsen_US
dc.subjectartificial intelligenceen_US
dc.subjectmachine learningen_US
dc.subjectcomputational linguisticsen_US
dc.subjectnatural language processingen_US
dc.subjecthuman language technologyen_US
dc.subjecttext processingen_US
dc.subjectactive learningen_US
dc.subjectselective samplingen_US
dc.subjectquery learningen_US
dc.subjectbinary classificationen_US
dc.subjecttext classificationen_US
dc.subjectnamed entity classificationen_US
dc.subjectbiomedical named entity classificationen_US
dc.subjectannotation bottlenecken_US
dc.subjectannotation costsen_US
dc.subjectstopping criteriaen_US
dc.subjectstopping methodsen_US
dc.subjectstabilizing predictionsen_US
dc.subjectagreement metricsen_US
dc.subjectagreement statisticsen_US
dc.subjectcontingency table analysisen_US
dc.subjectstop seten_US
dc.subjectstop set constructionen_US
dc.subjectKappa statisticen_US
dc.subjectCohen's Kappaen_US
dc.subjectinter-model agreementen_US
dc.subjectF-measureen_US
dc.subjectF-scoreen_US
dc.subjectannotation/performance tradeoffen_US
dc.subjectaggressive stoppingen_US
dc.subjectconservative stoppingen_US
dc.subjectuser-adjustable stoppingen_US
dc.subjectsupport vector machinesen_US
dc.subjectSVMsen_US
dc.titleA Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stoppingen_US
dc.typeArticleen_US

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