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Bringing Active Learning to Life

  • Active learning has been applied to different NLP tasks, with the aim of limiting the amount of time and cost for human annotation. Most studies on active learning have only simulated the annotation scenario, using prelabelled gold standard data. We present the first active learning experiment for Word Sense Disambiguation with human annotators in a realistic environment, using fine-grained sense distinctions, and investigate whether AL can reduce annotation cost and boost classifier performance when applied to a real-world task.

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Metadaten
Author:Ines Rehbein, Josef RuppenhoferGND, Alexis Palmer
URN:urn:nbn:de:bsz:mh39-52945
URL:http://www.aclweb.org/anthology/C/C10/
Parent Title (English):23rd International Conference on Computational Linguistics. Proceedings of the Conference
Publisher:Tsinghua University Press
Place of publication:Beijing
Document Type:Conference Proceeding
Language:English
Year of first Publication:2010
Date of Publication (online):2016/09/22
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
Tag:Active learning
GND Keyword:Annotation; Computerlinguistik
Issue:2
First Page:949
Last Page:957
Dewey Decimal Classification:400 Sprache / 410 Linguistik
Linguistics-Classification:Computerlinguistik
Open Access?:Ja
Licence (German):License LogoCreative Commons - Namensnennung-Keine kommerzielle Nutzung-Weitergabe unter gleichen Bedingungen 3.0 Deutschland