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    <titleInfo>
        <title>Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff</title>
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        <namePart type="given">Matthias</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Satoshi</namePart>
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    <name type="personal">
        <namePart type="given">Alex</namePart>
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    <abstract>In this paper, we study the problem of manually correcting automatic annotations of natural language in as efficient a manner as possible. We introduce a method for automatically segmenting a corpus into chunks such that many uncertain labels are grouped into the same chunk, while human supervision can be omitted altogether for other segments. A tradeoff must be found for segment sizes. Choosing short segments allows us to reduce the number of highly confident labels that are supervised by the annotator, which is useful because these labels are often already correct and supervising correct labels is a waste of effort. In contrast, long segments reduce the cognitive effort due to context switches. Our method helps find the segmentation that optimizes supervision efficiency by defining user models to predict the cost and utility of supervising each segment and solving a constrained optimization problem balancing these contradictory objectives. A user study demonstrates noticeable gains over pre-segmented, confidence-ordered baselines on two natural language processing tasks: speech transcription and word segmentation.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00174</identifier>
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        <date>2014</date>
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%0 Journal Article
%T Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff
%A Sperber, Matthias
%A Simantzik, Mirjam
%A Neubig, Graham
%A Nakamura, Satoshi
%A Waibel, Alex
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F sperber-etal-2014-segmentation
%X In this paper, we study the problem of manually correcting automatic annotations of natural language in as efficient a manner as possible. We introduce a method for automatically segmenting a corpus into chunks such that many uncertain labels are grouped into the same chunk, while human supervision can be omitted altogether for other segments. A tradeoff must be found for segment sizes. Choosing short segments allows us to reduce the number of highly confident labels that are supervised by the annotator, which is useful because these labels are often already correct and supervising correct labels is a waste of effort. In contrast, long segments reduce the cognitive effort due to context switches. Our method helps find the segmentation that optimizes supervision efficiency by defining user models to predict the cost and utility of supervising each segment and solving a constrained optimization problem balancing these contradictory objectives. A user study demonstrates noticeable gains over pre-segmented, confidence-ordered baselines on two natural language processing tasks: speech transcription and word segmentation.
%9 journal article
%R 10.1162/tacl_a_00174
%U https://aclanthology.org/Q14-1014
%U https://doi.org/10.1162/tacl_a_00174
%P 169-180

Markdown (Informal)

[Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff](https://aclanthology.org/Q14-1014) (Sperber et al., TACL 2014)

• Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff (Sperber et al., TACL 2014)

ACL

• Matthias Sperber, Mirjam Simantzik, Graham Neubig, Satoshi Nakamura, and Alex Waibel. 2014. Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff. Transactions of the Association for Computational Linguistics, 2:169–180.