Investigating the effects of selective sampling on the annotation task

Ben Hachey*, Beatrice Alex, Markus Becker

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

21 Citations (Scopus)

Abstract

We report on an active learning experiment for named entity recognition in the astronomy domain. Active learning has been shown to reduce the amount of labelled data required to train a supervised learner by selectively sampling more informative data points for human annotation. We inspect double annotation data from the same domain and quantify potential problems concerning annotators' performance. For data selectively sampled according to different selection metrics, we find lower inter-annotator agreement and higher per token annotation times. However, overall results confirm the utility of active learning.

Original languageEnglish
Title of host publicationCoNLL 2005 - Proceedings of the Ninth Conference on Computational Natural Language Learning
Pages144-151
Number of pages8
Publication statusPublished - 2005
Event9th Conference on Computational Natural Language Learning, CoNLL - 2005 - Ann Arbor, United States
Duration: 29 Jun 200530 Jun 2005

Other

Other9th Conference on Computational Natural Language Learning, CoNLL - 2005
CountryUnited States
CityAnn Arbor
Period29/06/0530/06/05

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