Attribute Extraction For Classification

Attribute Extraction for Classification is a computational method that identifies and isolates distinguishing semantic or syntactic features from product descriptions or textual data to enable their systematic categorization into predefined classes based on intrinsic characteristics.

33

Publications

2.5K

Citations

105

Authors

49

Institutions

Publications per year

2017–2026

18

Authors

105

  1. MP
    Marius Paşca

    Google (United States), United States

    3 publications · 121 citations

    3

    h-index

  2. NZ
    Ning Zhang

    Chinese Academy of Sciences, China

    2 publications · 523 citations

    2

    h-index

  3. TD
    Trevor Darrell

    University of California, Berkeley, United States

    2 publications · 523 citations

    2

    h-index

  4. BV
    Benjamin Van Durme

    Johns Hopkins University, United States

    2 publications · 148 citations

    2

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  5. LB
    Lubomir Bourdev

    University of California, Berkeley, United States

    2 publications · 523 citations

    2

    h-index

1–5 of 105

Venues

  1. Computer Speech & Language

    1 publications · 49 citations

    1

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  2. 1

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  3. World Wide Web

    1 publications · 12 citations

    1

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  4. Journal of Educational Psychology

    1 publications · 48 citations

    1

    h-index

  5. Cognitive Computation

    1 publications · 30 citations

    1

    h-index

1–5