Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DEXA: Supporting Non-Expert Annotators with Dynamic Examples from
  Experts   The success of crowdsourcing based annotation of text corpora depends on
ensuring that crowdworkers are sufficiently well-trained to perform the
annotation task accurately. To that end, a frequent approach to train
annotators is to provide instructions and a few example cases that demonstrate
how the task should be performed (referred to as the CONTROL approach). These
globally defined "task-level examples", however, (i) often only cover the
common cases that are encountered during an annotation task; and (ii) require
effort from crowdworkers during the annotation process to find the most
relevant example for the currently annotated sample. To overcome these
limitations, we propose to support workers in addition to task-level examples,
also with "task-instance level" examples that are semantically similar to the
currently annotated data sample (referred to as Dynamic Examples for
Annotation, DEXA). Such dynamic examples can be retrieved from collections
previously labeled by experts, which are usually available as gold standard
dataset. We evaluate DEXA on a complex task of annotating participants,
interventions, and outcomes (known as PIO) in sentences of medical studies. The
dynamic examples are retrieved using BioSent2Vec, an unsupervised semantic
sentence similarity method specific to the biomedical domain. Results show that
(i) workers of the DEXA approach reach on average much higher agreements
(Cohen's Kappa) to experts than workers of the the CONTROL approach (avg. of
0.68 to experts in DEXA vs. 0.40 in CONTROL); (ii) already three per majority
voting aggregated annotations of the DEXA approach reach substantial agreements
to experts of 0.78/0.75/0.69 for P/I/O (in CONTROL 0.73/0.58/0.46). Finally,
(iii) we acquire explicit feedback from workers and show that in the majority
of cases (avg. 72%) workers find the dynamic examples useful.

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The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: alg-geom