D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 59 Citations 14,244 194 World Ranking 1699 National Ranking 936

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Information retrieval
  • World Wide Web

Nick Craswell spends much of his time researching Information retrieval, Ranking, Relevance, World Wide Web and Search engine. His Information retrieval research integrates issues from Set and Artificial intelligence. His Ranking research is multidisciplinary, incorporating elements of Ranking, Web page and Rank.

His work carried out in the field of Relevance brings together such families of science as Hypermedia, Query language, Testbed and Server. His Web search query study, which is part of a larger body of work in World Wide Web, is frequently linked to 2d images, bridging the gap between disciplines. His Search engine study integrates concerns from other disciplines, such as Probabilistic logic, The Internet and Data mining.

His most cited work include:

  • An experimental comparison of click position-bias models (699 citations)
  • MS MARCO: A Human Generated MAchine Reading COmprehension Dataset (520 citations)
  • Random walks on the click graph (457 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Information retrieval, Artificial intelligence, Relevance, Ranking and Search engine. Nick Craswell studied Information retrieval and Ranking that intersect with Task. His research integrates issues of Machine learning and Natural language processing in his study of Artificial intelligence.

His studies deal with areas such as Web page, Data mining and Set as well as Relevance. Hyperlink is closely connected to Anchor text in his research, which is encompassed under the umbrella topic of Ranking. Nick Craswell has researched Search engine in several fields, including Information needs and Component.

He most often published in these fields:

  • Information retrieval (68.07%)
  • Artificial intelligence (22.27%)
  • Relevance (21.85%)

What were the highlights of his more recent work (between 2018-2021)?

  • Information retrieval (68.07%)
  • Artificial intelligence (22.27%)
  • Deep learning (10.92%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Information retrieval, Artificial intelligence, Deep learning, Ranking and Ranking. His Search engine and Information seeking study in the realm of Information retrieval connects with subjects such as Metric. His study in Search engine is interdisciplinary in nature, drawing from both Data-driven, Information needs, Component and Taxonomy.

His Artificial intelligence research is multidisciplinary, relying on both Test and Machine learning, Relevance. Many of his research projects under Deep learning are closely connected to Track with Track, tying the diverse disciplines of science together. Nick Craswell combines subjects such as Training set and Rank with his study of Ranking.

Between 2018 and 2021, his most popular works were:

  • An Introduction to Neural Information Retrieval (126 citations)
  • Overview of the TREC 2019 deep learning track. (79 citations)
  • An Updated Duet Model for Passage Re-ranking (26 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Information retrieval
  • Statistics

His primary areas of investigation include Information retrieval, Ranking, Ranking, Artificial intelligence and Document retrieval. His study involves Search engine and Information seeking, a branch of Information retrieval. His studies in Search engine integrate themes in fields like Information needs and Component.

His work is dedicated to discovering how Ranking, Relevance are connected with Test and other disciplines. The study incorporates disciplines such as User interface, Training set, Pooling and Transformer in addition to Document retrieval. His Artificial neural network research incorporates themes from Learning to rank, Set and Natural language.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

An experimental comparison of click position-bias models

Nick Craswell;Onno Zoeter;Michael Taylor;Bill Ramsey.
web search and data mining (2008)

870 Citations

Random walks on the click graph

Nick Craswell;Martin Szummer.
international acm sigir conference on research and development in information retrieval (2007)

541 Citations

Overview of the TREC-2002 Web Track.

Nick Craswell;David Hawking.
text retrieval conference (2002)

479 Citations

Overview of the TREC 2009 Web Track

Charles L. A. Clarke;Nick Craswell;Ian Soboroff.
text retrieval conference (2009)

422 Citations

Overview of the TREC-8 Web Track

David Hawking;Ellen M. Voorhees;Nick Craswell;Peter Bailey.
Overview of the TREC-8 Web Track (2000)

411 Citations

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Payal Bajaj;Daniel Campos;Nick Craswell;Li Deng.
arXiv: Computation and Language (2016)

408 Citations

Overview of the TREC-2005 Enterprise Track

Nick Craswell;Arjen P. de Vries;Ian Soboroff.
text retrieval conference (2005)

399 Citations

Effective site finding using link anchor information

Nick Craswell;David Hawking;Stephen Robertson.
international acm sigir conference on research and development in information retrieval (2001)

395 Citations

Learning to Match using Local and Distributed Representations of Text for Web Search

Bhaskar Mitra;Fernando Diaz;Nick Craswell.
the web conference (2017)

312 Citations

Measuring Search Engine Quality

David Hawking;Nick Craswell;Peter Bailey;Kathleen Griffihs.
Information Retrieval (2001)

289 Citations

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