The Provo Corpus: A large eye-tracking corpus with predictability norms

被引:0
|
作者
Steven G. Luke
Kiel Christianson
机构
[1] Brigham Young University,Department of Psychology and Neuroscience Center
[2] University of Illinois at Urbana-Champaign,undefined
[3] The Beckman Institute for Advanced Science and Technology,undefined
来源
Behavior Research Methods | 2018年 / 50卷
关键词
Corpus study; Eyetracking; Reading; Predictability;
D O I
暂无
中图分类号
学科分类号
摘要
This article presents the Provo Corpus, a corpus of eye-tracking data with accompanying predictability norms. The predictability norms for the Provo Corpus differ from those of other corpora. In addition to traditional cloze scores that estimate the predictability of the full orthographic form of each word, the Provo Corpus also includes measures of the predictability of the morpho-syntactic and semantic information for each word. This makes the Provo Corpus ideal for studying predictive processes in reading. Some analyses using these data have previously been reported elsewhere (Luke & Christianson, 2016). The Provo Corpus is available for download on the Open Science Framework, at https://osf.io/sjefs.
引用
收藏
页码:826 / 833
页数:7
相关论文
共 50 条
  • [1] The Provo Corpus: A large eye-tracking corpus with predictability norms
    Luke, Steven G.
    Christianson, Kiel
    [J]. BEHAVIOR RESEARCH METHODS, 2018, 50 (02) : 826 - 833
  • [2] RastrOS Project: Natural Language Processing contributions to the development of an eye-tracking corpus with predictability norms for Brazilian Portuguese
    Leal, Sidney Evaldo
    Lukasova, Katerina
    Carthery-Goulart, Maria Teresa
    Aluisio, Sandra Maria
    [J]. LANGUAGE RESOURCES AND EVALUATION, 2022, 56 (04) : 1333 - 1372
  • [3] RastrOS Project: Natural Language Processing contributions to the development of an eye-tracking corpus with predictability norms for Brazilian Portuguese
    Sidney Evaldo Leal
    Katerina Lukasova
    Maria Teresa Carthery-Goulart
    Sandra Maria Aluísio
    [J]. Language Resources and Evaluation, 2022, 56 : 1333 - 1372
  • [4] The Beijing Sentence Corpus: A Chinese sentence corpus with eye movement data and predictability norms
    Jinger Pan
    Ming Yan
    Eike M. Richter
    Hua Shu
    Reinhold Kliegl
    [J]. Behavior Research Methods, 2022, 54 : 1989 - 2000
  • [5] The Beijing Sentence Corpus: A Chinese sentence corpus with eye movement data and predictability norms
    Pan, Jinger
    Yan, Ming
    Richter, Eike M.
    Shu, Hua
    Kliegl, Reinhold
    [J]. BEHAVIOR RESEARCH METHODS, 2022, 54 (04) : 1989 - 2000
  • [6] Building an ACT-R Reader for Eye-Tracking Corpus Data
    Dotlacil, Jakub
    [J]. TOPICS IN COGNITIVE SCIENCE, 2018, 10 (01) : 144 - 160
  • [7] GECO-MT: The Ghent Eye-tracking Corpus of Machine Translation
    Colman, Toon
    Fonteyne, Margot
    Daems, Joke
    Dirix, Nicolas
    Macken, Lieve
    [J]. LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2022, : 29 - 38
  • [8] Role of Expectation and Working Memory Constraints in Hindi Comprehension: An Eye-tracking Corpus Analysis
    Agrawal, Arpit
    Agarwal, Sumeet
    Husain, Samar
    [J]. JOURNAL OF EYE MOVEMENT RESEARCH, 2017, 10 (02):
  • [9] Integration and prediction difficulty in Hindi sentence comprehension: Evidence from an eye-tracking corpus
    Husain, Samar
    Vasishth, Shravan
    Srinivasan, Narayanan
    [J]. JOURNAL OF EYE MOVEMENT RESEARCH, 2015, 8 (02):
  • [10] Eye-tracking and corpus-based analyses of syntax-semantics interactions in complement coercion
    Lowder, Matthew W.
    Gordon, Peter C.
    [J]. LANGUAGE COGNITION AND NEUROSCIENCE, 2016, 31 (07) : 921 - 939