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.
Author: | Ines Rehbein, Josef RuppenhoferGND, Alexis Palmer |
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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 |
DDC classes: | 400 Sprache / 410 Linguistik |
Open Access?: | ja |
Linguistics-Classification: | Computerlinguistik |
Licence (German): | Creative Commons - Namensnennung-Keine kommerzielle Nutzung-Weitergabe unter gleichen Bedingungen 3.0 Deutschland |