Tikkoun sofrim: A Webapp for personalization and adaptation of crowdsourcing transcriptions

Alan J. Wecker, Uri Schor, Dror Elovits, Daniel Stoekl Ben Ezra, Vered Raziel-Kretzmer, Tsvi Kuflik, Avigail Ohali, Moshe Lavee, Lily Signoret

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper briefly describes aspects of the Tikkoun Sofrim crowdsourcing webApp. Tikkoun Sofrim is a webApp which allows users to correct automatic transcriptions (AT) done by an AI Neural network engine. We look at the background of the crowdsourcing phenomenon in the use of automatic transcription of digital humanities documents. System structure is briefly described. We then examine personalization and adaption aspects at different stages of the user/application lifecycle Finally, we briefly outline future challenges.

Original languageEnglish
Title of host publicationACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages109-110
Number of pages2
ISBN (Electronic)9781450367110
DOIs
StatePublished - 6 Jun 2019
Event27th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2019 - Larnaca, Cyprus
Duration: 9 Jun 201912 Jun 2019

Publication series

NameACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization

Conference

Conference27th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2019
Country/TerritoryCyprus
CityLarnaca
Period9/06/1912/06/19

Bibliographical note

Funding Information:
The project is funded by PHC Maimonide 41146YC

Publisher Copyright:
© 2019 Copyright is held by the owner/author(s).

Keywords

  • Crowdsourcing
  • Digital humanities
  • Midrash tanhuma
  • Transcription

ASJC Scopus subject areas

  • Software

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