@inproceedings{felt-etal-2014-using,
title = "Using Transfer Learning to Assist Exploratory Corpus Annotation",
author = "Felt, Paul and
Ringger, Eric and
Seppi, Kevin and
Heal, Kristian",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/147_Paper.pdf",
pages = "140--145",
abstract = "We describe an under-studied problem in language resource management: that of providing automatic assistance to annotators working in exploratory settings. When no satisfactory tagset already exists, such as in under-resourced or undocumented languages, it must be developed iteratively while annotating data. This process naturally gives rise to a sequence of datasets, each annotated differently. We argue that this problem is best regarded as a transfer learning problem with multiple source tasks. Using part-of-speech tagging data with simulated exploratory tagsets, we demonstrate that even simple transfer learning techniques can significantly improve the quality of pre-annotations in an exploratory annotation.",
}
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%0 Conference Proceedings
%T Using Transfer Learning to Assist Exploratory Corpus Annotation
%A Felt, Paul
%A Ringger, Eric
%A Seppi, Kevin
%A Heal, Kristian
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 may
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F felt-etal-2014-using
%X We describe an under-studied problem in language resource management: that of providing automatic assistance to annotators working in exploratory settings. When no satisfactory tagset already exists, such as in under-resourced or undocumented languages, it must be developed iteratively while annotating data. This process naturally gives rise to a sequence of datasets, each annotated differently. We argue that this problem is best regarded as a transfer learning problem with multiple source tasks. Using part-of-speech tagging data with simulated exploratory tagsets, we demonstrate that even simple transfer learning techniques can significantly improve the quality of pre-annotations in an exploratory annotation.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/147_Paper.pdf
%P 140-145
Markdown (Informal)
[Using Transfer Learning to Assist Exploratory Corpus Annotation](http://www.lrec-conf.org/proceedings/lrec2014/pdf/147_Paper.pdf) (Felt et al., LREC 2014)
ACL