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Sequence labelling and sequence classification with gaze: Novel uses of eye‐tracking data for Natural Language Processing

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Sequence labelling and sequence classification with gaze : Novel uses of eye‐tracking data for Natural Language Processing. / Barrett, Maria Jung; Hollenstein, Nora.

In: Language and Linguistics Compass, Vol. 14, No. 11, 05.11.2020, p. 1-16.

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

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@article{c1c37a5ea0a04919a1b9f31c6f0d5ced,
title = "Sequence labelling and sequence classification with gaze: Novel uses of eye‐tracking data for Natural Language Processing",
abstract = "Eye‐tracking data from reading provide a structured signal with a fine‐grained temporal resolution which closely follows the sequential structure of the text. It is highly correlated with the cognitive load associated with different stages of human, cognitive text processing. While eye‐tracking data have been extensively studied to understand human cognition, it has only recently been considered for Natural Language Processing (NLP). In this review, we provide a comprehensive overview of how gaze data are being used in data‐driven NLP, in particular for sequence labelling and sequence classification tasks. We argue that eye‐tracking may effectively counter one of the core challenges of machine‐learning‐based NLP: the scarcity of annotated data. We outline the recent advances in gaze‐augmented NLP to discuss how the gaze signal from human readers can be leveraged while also considering the potentials and limitations of this data source.",
author = "Barrett, {Maria Jung} and Nora Hollenstein",
year = "2020",
month = nov,
day = "5",
doi = "https://doi.org/10.1111/lnc3.12396",
language = "English",
volume = "14",
pages = "1--16",
journal = "Language and Linguistics Compass",
issn = "1749-818X",
publisher = "Wiley-Blackwell",
number = "11",

}

RIS

TY - JOUR

T1 - Sequence labelling and sequence classification with gaze

T2 - Novel uses of eye‐tracking data for Natural Language Processing

AU - Barrett, Maria Jung

AU - Hollenstein, Nora

PY - 2020/11/5

Y1 - 2020/11/5

N2 - Eye‐tracking data from reading provide a structured signal with a fine‐grained temporal resolution which closely follows the sequential structure of the text. It is highly correlated with the cognitive load associated with different stages of human, cognitive text processing. While eye‐tracking data have been extensively studied to understand human cognition, it has only recently been considered for Natural Language Processing (NLP). In this review, we provide a comprehensive overview of how gaze data are being used in data‐driven NLP, in particular for sequence labelling and sequence classification tasks. We argue that eye‐tracking may effectively counter one of the core challenges of machine‐learning‐based NLP: the scarcity of annotated data. We outline the recent advances in gaze‐augmented NLP to discuss how the gaze signal from human readers can be leveraged while also considering the potentials and limitations of this data source.

AB - Eye‐tracking data from reading provide a structured signal with a fine‐grained temporal resolution which closely follows the sequential structure of the text. It is highly correlated with the cognitive load associated with different stages of human, cognitive text processing. While eye‐tracking data have been extensively studied to understand human cognition, it has only recently been considered for Natural Language Processing (NLP). In this review, we provide a comprehensive overview of how gaze data are being used in data‐driven NLP, in particular for sequence labelling and sequence classification tasks. We argue that eye‐tracking may effectively counter one of the core challenges of machine‐learning‐based NLP: the scarcity of annotated data. We outline the recent advances in gaze‐augmented NLP to discuss how the gaze signal from human readers can be leveraged while also considering the potentials and limitations of this data source.

U2 - https://doi.org/10.1111/lnc3.12396

DO - https://doi.org/10.1111/lnc3.12396

M3 - Journal article

VL - 14

SP - 1

EP - 16

JO - Language and Linguistics Compass

JF - Language and Linguistics Compass

SN - 1749-818X

IS - 11

ER -

ID: 85544652