Active Learning for Sense Annotation

Hector Martinez Alonso; Barbara Plank; Anders Trærup Johannsen; Anders Søgaard

Active Learning for Sense Annotation

Hector Martinez Alonso, Barbara Plank, Anders Trærup Johannsen, Anders Søgaard

LUKKET: Center for Sprogteknologi

Abstract

This article describes a real (nonsynthetic) active-learning experiment to obtain supersense annotations for Danish. We compare two instance selection strategies, namely lowest-prediction confidence (MAX), and sampling from the confidence distribution (SAMPLE). We evaluate their performance during the annotation process, across domains for the final resulting system, as well as against in-domain adjudicated data. The SAMPLE strategy yields competitive models that are more robust than the overly length-biased selection criterion of MAX.

Originalsprog	Engelsk
Titel	Proceedings of the 20th Nordic Conference of Computational Linguistics : NODALIDA 2015
Antal sider	5
Udgivelsessted	Linköping
Forlag	Linköping University Electronic Press
Publikationsdato	2015
Sider	245-250
ISBN (Elektronisk)	978-91-7519-098-3
Status	Udgivet - 2015

Navn	NEALT Proceedings Series
Vol/bind	23
ISSN	1736-6305

Citationsformater

Active Learning for Sense Annotation. / Martinez Alonso, Hector; Plank, Barbara; Johannsen, Anders Trærup et al.
Proceedings of the 20th Nordic Conference of Computational Linguistics: NODALIDA 2015. Linköping: Linköping University Electronic Press, 2015. s. 245-250 (NEALT Proceedings Series, Bind 23).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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AB - This article describes a real (nonsynthetic) active-learning experiment to obtain supersense annotations for Danish. We compare two instance selection strategies, namely lowest-prediction confidence (MAX), and sampling from the confidence distribution (SAMPLE). We evaluate their performance during the annotation process, across domains for the final resulting system, as well as against in-domain adjudicated data. The SAMPLE strategy yields competitive models that are more robust than the overly length-biased selection criterion of MAX.

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Active Learning for Sense Annotation

Abstract

Fingeraftryk

Citationsformater