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Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events.

, , , , and . CIKM, page 1201-1210. ACM, (2015)

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What You Sow, So Shall You Reap! Toward Preselection Mechanisms for Macrotask Crowdsourcing., and . Macrotask Crowdsourcing, Springer, (2019)Technology Enhancing Learning: Past, Present and Future., , and . EC-TEL, volume 8719 of Lecture Notes in Computer Science, page 193-206. Springer, (2014)Make Hay While the Crowd Shines: Towards Efficient Crowdsourcing on the Web.. WWW (Companion Volume), page 493-497. ACM, (2015)Its Getting Crowded! Verbesserung der Effektivität von Microtask Crowdsourcing.. Ausgezeichnete Informatikdissertationen, volume D-18 of LNI, GI, (2017)Bias in data-driven artificial intelligence systems - An introductory survey, , , , , , , , , and 13 other author(s). Wiley Interdiscip. Rev. Data Min. Knowl. Discov., (2020)Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events., , , , and . CoRR, (2017)Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events., , , , and . CIKM, page 1201-1210. ACM, (2015)Improving Entity Retrieval on Structured Data., , and . International Semantic Web Conference (1), volume 9366 of Lecture Notes in Computer Science, page 474-491. Springer, (2015)Towards Preservation of Semantically Enriched Architectural Knowledge., , , , , and . SDA, volume 1091 of CEUR Workshop Proceedings, page 4-15. CEUR-WS.org, (2013)Crawl Me Maybe: Iterative Linked Dataset Preservation., , and . International Semantic Web Conference (Posters & Demos), volume 1272 of CEUR Workshop Proceedings, page 433-436. CEUR-WS.org, (2014)