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pro vyhledávání: '"Wurzer, Dominik"'
Autor:
Wurzer, Dominik, Qin, Yumeng
Publikováno v:
SIGIR 20, July 2020
First Story Detection describes the task of identifying new events in a stream of documents. The UMass-FSD system is known for its strong performance in First Story Detection competitions. Recently, it has been frequently used as a high accuracy base
Externí odkaz:
http://arxiv.org/abs/2208.01347
Autor:
Wurzer, Dominik, Qin, Yumeng
Publikováno v:
SIGIR 18, July 2018, Ann Arbor, MI, USA
Kterm Hashing provides an innovative approach to novelty detection on massive data streams. Previous research focused on maximizing the efficiency of Kterm Hashing and succeeded in scaling First Story Detection to Twitter-size data stream without sac
Externí odkaz:
http://arxiv.org/abs/2208.01340
Autor:
Wurzer, Dominik Stefan
In today’s world the internet and social media are omnipresent and information is accessible to everyone. This shifted the advantage from those who have access to information to those who do so first. Identifying new events as they emerge is of sub
Externí odkaz:
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738972
Autor:
Wurzer, Dominik, Qin, Yumeng
Newswire and Social Media are the major sources of information in our time. While the topical demographic of Western Media was subjects of studies in the past, less is known about Chinese Media. In this paper, we apply event detection and tracking te
Externí odkaz:
http://arxiv.org/abs/1701.01737
Rumour detection is hard because the most accurate systems operate retrospectively, only recognizing rumours once they have collected repeated signals. By then the rumours might have already spread and caused harm. We introduce a new category of feat
Externí odkaz:
http://arxiv.org/abs/1611.06322
Relevance Models are well-known retrieval models and capable of producing competitive results. However, because they use query expansion they can be very slow. We address this slowness by incorporating two variants of locality sensitive hashing (LSH)
Externí odkaz:
http://arxiv.org/abs/1607.02641
Publikováno v:
Chinese Journal of Electronics. 27:514-520
Recent uproar of fake news and misinformation on social media platforms has sparked the interest in the scientific community to automatically detect and refute them. The most popular research task to counteract misinformation, Rumour detection, requi
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