Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Extended Dawid-Skene Model: Fusing Information from Multiple Data
  Schemas   While label fusion from multiple noisy annotations is a well understood
concept in data wrangling (tackled for example by the Dawid-Skene (DS) model),
we consider the extended problem of carrying out learning when the labels
themselves are not consistently annotated with the same schema. We show that
even if annotators use disparate, albeit related, label-sets, we can still draw
inferences for the underlying full label-set. We propose the Inter-Schema
AdapteR (ISAR) to translate the fully-specified label-set to the one used by
each annotator, enabling learning under such heterogeneous schemas, without the
need to re-annotate the data. We apply our method to a mouse behavioural
dataset, achieving significant gains (compared with DS) in out-of-sample
log-likelihood (-3.40 to -2.39) and F1-score (0.785 to 0.864).

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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin