Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Entity Recognition at First Sight: Improving NER with Eye Movement
  Information   Previous research shows that eye-tracking data contains information about the
lexical and syntactic properties of text, which can be used to improve natural
language processing models. In this work, we leverage eye movement features
from three corpora with recorded gaze information to augment a state-of-the-art
neural model for named entity recognition (NER) with gaze embeddings. These
corpora were manually annotated with named entity labels. Moreover, we show how
gaze features, generalized on word type level, eliminate the need for recorded
eye-tracking data at test time. The gaze-augmented models for NER using
token-level and type-level features outperform the baselines. We present the
benefits of eye-tracking features by evaluating the NER models on both
individual datasets as well as in cross-domain settings.

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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph