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Nick Craswell
Researcher at Microsoft
Publications - 250
Citations - 13940
Nick Craswell is an academic researcher from Microsoft. The author has contributed to research in topics: Ranking (information retrieval) & Relevance (information retrieval). The author has an hindex of 59, co-authored 238 publications receiving 12463 citations. Previous affiliations of Nick Craswell include Australian National University & University of Massachusetts Amherst.
Papers
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Proceedings ArticleDOI
An experimental comparison of click position-bias models
TL;DR: A cascade model, where users view results from top to bottom and leave as soon as they see a worthwhile document, is the best explanation for position bias in early ranks.
Posted Content
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Payal Bajaj,Daniel Campos,Nick Craswell,Li Deng,Jianfeng Gao,Xiaodong Liu,Rangan Majumder,Andrew McNamara,Bhaskar Mitra,Tri Nguyen,Mir Rosenberg,Xia Song,Alina Stoica,Saurabh Tiwary,Tong Wang +14 more
TL;DR: This new dataset is aimed to overcome a number of well-known weaknesses of previous publicly available datasets for the same task of reading comprehension and question answering, and is the most comprehensive real-world dataset of its kind in both quantity and quality.
Proceedings ArticleDOI
Random walks on the click graph
Nick Craswell,Martin Szummer +1 more
TL;DR: A Markov random walk model is applied to a large click log, producing a probabilistic ranking of documents for a given query, demonstrating its ability to retrieve relevant documents that have not yet been clicked for that query and rank those effectively.
Proceedings ArticleDOI
Learning to Match using Local and Distributed Representations of Text for Web Search
TL;DR: This work proposes a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that Matching with distributed representations complements matching with traditional local representations.
Proceedings Article
Overview of the TREC-2005 Enterprise Track
TL;DR: The goal of the enterprise track is to conduct experiments with enterprise data that reflect the experiences of users in real organisations, such that for example, an email ranking technique that is effective here would be a good choice for deployment in a real multi-user email search application.