Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: One Size Does Not Fit All: The Case for Personalised Word Complexity
  Models   Complex Word Identification (CWI) aims to detect words within a text that a
reader may find difficult to understand. It has been shown that CWI systems can
improve text simplification, readability prediction and vocabulary acquisition
modelling. However, the difficulty of a word is a highly idiosyncratic notion
that depends on a reader's first language, proficiency and reading experience.
In this paper, we show that personal models are best when predicting word
complexity for individual readers. We use a novel active learning framework
that allows models to be tailored to individuals and release a dataset of
complexity annotations and models as a benchmark for further research.

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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg