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The 1st International Workshop on Implicit Author Characterization from Texts for Search and Retrieval (IACT'23)

Published: 18 July 2023 Publication History

Abstract

The first edition of the Implicit Author Characterization from Texts for Search and Retrieval (IACT'23) aims at bringing to the forefront the challenges involved in identifying and extracting from texts implicit information about authors (e.g., human or AI) and using it in IR tasks. The IACT workshop provides a common forum to consolidate multi-disciplinary efforts and foster discussions to identify the wide-ranging issues related to the task of extracting implicit author-related information from the textual content, including novel tasks and datasets. We will also discuss the ethical implications of implicit information extraction. In addition, we announce a shared task focused on automatically determining the literary epochs of written books.

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  • (2024)Automatic Gender Identification from TextApplied Sciences10.3390/app14241204114:24(12041)Online publication date: 23-Dec-2024

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cover image ACM Conferences
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2023
3567 pages
ISBN:9781450394086
DOI:10.1145/3539618
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 18 July 2023

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Author Tags

  1. ai-generated content
  2. author characterization
  3. ethics
  4. implicit information retrieval
  5. text classification

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  • Extended-abstract

Funding Sources

  • National Funds through the FCT ð Fundação para a Ciência e a Tecnologia I.P. (Portuguese Foundation for Science and Technology)
  • ERDF through the NORTE 2020 under the PORTUGAL 2020 and by National Funds through the Portuguese funding agency FCT

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SIGIR '23
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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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  • (2024)Automatic Gender Identification from TextApplied Sciences10.3390/app14241204114:24(12041)Online publication date: 23-Dec-2024

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