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Enhancing keyphrase extraction from microblogs using human reading time.

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Title: Enhancing keyphrase extraction from microblogs using human reading time.
Authors: Zhang, Yingyi1, Zhang, Chengzhi1 zhangcz@njust.edu.cn
Source: Journal of the Association for Information Science & Technology. May2021, Vol. 72 Issue 5, p611-626. 16p. 2 Diagrams, 8 Charts, 2 Graphs.
Subjects: Eye movements, Information retrieval, Research funding, Attention, Artificial neural networks, Reading, Blogs
Abstract: The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations (FDs) extracted from an open source eye‐tracking corpus. Moreover, we propose strategies to make eye FD more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Association for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Enhancing keyphrase extraction from microblogs using human reading time.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Yingyi%22">Zhang, Yingyi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chengzhi%22">Zhang, Chengzhi</searchLink><relatesTo>1</relatesTo><i> zhangcz@njust.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Association+for+Information+Science+%26+Technology%22">Journal of the Association for Information Science & Technology</searchLink>. May2021, Vol. 72 Issue 5, p611-626. 16p. 2 Diagrams, 8 Charts, 2 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Eye+movements%22">Eye movements</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Reading%22">Reading</searchLink><br /><searchLink fieldCode="DE" term="%22Blogs%22">Blogs</searchLink>
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  Label: Abstract
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  Data: The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations (FDs) extracted from an open source eye‐tracking corpus. Moreover, we propose strategies to make eye FD more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of the Association for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/asi.24430
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 611
    Subjects:
      – SubjectFull: Eye movements
        Type: general
      – SubjectFull: Information retrieval
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Attention
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Reading
        Type: general
      – SubjectFull: Blogs
        Type: general
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      – TitleFull: Enhancing keyphrase extraction from microblogs using human reading time.
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            – D: 01
              M: 05
              Text: May2021
              Type: published
              Y: 2021
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