RastrOS Project: Natural Language Processing contributions to the development of an eye-tracking corpus with predictability norms for Brazilian Portuguese.

This article presents RastrOS, a new eye-tracking corpus of eye movement data from university students during silent reading of paragraphs of texts in Brazilian Portuguese (BP). The article shows the potential of the corpus for natural language processing (NLP) using it to evaluate the sentence comp...

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Publicado en:Language Resources & Evaluation Vol. 56; no. 4; pp. 1333 - 1373
Autores principales: Leal, Sidney Evaldo, Lukasova, Katerina, Carthery-Goulart, Maria Teresa, Aluísio, Sandra Maria
Formato: Artículo
Publicado: Springer Nature Dec2022
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Acceso en línea:Ver este registro en EBSCOhost
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          Leal, Sidney Evaldo
          Lukasova, Katerina
          Carthery-Goulart, Maria Teresa
          Aluísio, Sandra Maria
        affil:
          Instituto de Ciências Matemáticas e de Computação - University of São Paulo, São Paulo, Brazil
          Center of Mathematics, Computing and Cognition, Federal University of ABC, São Paulo, Brazil
      su:
        Open Software Foundation
        Natural language processing
        Portuguese language
        Eye tracking
        Similarity (Psychology)
        Silent reading
        Corpora
        Mental representation
      sug:
        subj:
          Open Software Foundation
          Natural language processing
          Portuguese language
          Eye tracking
          Similarity (Psychology)
          Silent reading
          Corpora
          Mental representation
      keyword:
        Brazilian Portuguese
        Eye-tracking corpus
        Predictability norms
        Sentence complexity prediction
      ab: This article presents RastrOS, a new eye-tracking corpus of eye movement data from university students during silent reading of paragraphs of texts in Brazilian Portuguese (BP). The article shows the potential of the corpus for natural language processing (NLP) using it to evaluate the sentence complexity prediction task in BP and it also focuses on the description of NLP resources and methods developed to create the corpus. Specifically, we present: (i) the method used to select the corpus paragraphs from large corpora, using linguistic metrics and clustering algorithms; (ii) the platform for collecting the Cloze test, which is also responsible for creating the project datasets, and (iii) the hybrid semantic similarity method, based on word embedding models and contextualised word representations, used to generate semantic predictability norms. RastrOS can be downloaded from the open science framework repository with the computational infrastructure mentioned above. Datasets with predictability norms of 393 participants and eye-tracking data of 37 participants are available in the OSF repository for this work (https://osf.io/9jxg3/).
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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