Teachable Facets: A Framework of Interactive Machine Teaching for Information Filtering-Reference-Cited by-同舟云学术

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Teachable Facets: A Framework of Interactive Machine Teaching for Information Filtering

Published:2024-03-10 Issue: Volume:2 Page:178-188
ISSN:
Container-title:Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval
language:
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Author:

Mishra Swati¹^ORCID,Ryerkerk Matt²^ORCID,Lockerman Yitzchak²^ORCID,Eis David²^ORCID,Rzeszotarski Jeffrey M³^ORCID

Affiliation:

1. Cornell University, USA

2. Bloomberg, United States

3. Cornell University, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3627508.3638289

Reference69 articles.

1. Visual information seeking

2. Stuart Berg, Dominik Kutra, Thorben Kroeger, Christoph N Straehle, Bernhard X Kausler, Carsten Haubold, Martin Schiegg, Janez Ales, Thorsten Beier, Markus Rudy, 2019. Ilastik: interactive machine learning for (bio) image analysis. Nature methods 16, 12 (2019), 1226–1232.

3. Laura Ana Maria Bostan, Evgeny Kim, and Roman Klinger. 2020. GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception. In Proceedings of the Twelfth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, 1554–1566. https://aclanthology.org/2020.lrec-1.194

4. Newsworthiness and story prominence: How the presence of news factors relates to upfront position and length of news stories

5. Rethinking news values: What a discursive approach can tell us about the construction of news discourse and news photography

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