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 32 Citations 6,355 65 World Ranking 7233 National Ranking 3399

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Information retrieval
  • Machine learning

His scientific interests lie mostly in Information retrieval, Ranking, Artificial intelligence, Machine learning and Ranking. As part of one scientific family, Hugo Zaragoza deals mainly with the area of Information retrieval, narrowing it down to issues related to the World Wide Web, and often Human–computer information retrieval. Hugo Zaragoza interconnects Web search engine and Data mining in the investigation of issues within Ranking.

He mostly deals with Learning to rank in his studies of Artificial intelligence. His Ranking research is multidisciplinary, incorporating perspectives in Feature, Training set, Factoid and Identification. The concepts of his Relevance study are interwoven with issues in Probabilistic logic and Relevance feedback.

His most cited work include:

  • The Probabilistic Relevance Framework (952 citations)
  • Simple BM25 extension to multiple weighted fields (549 citations)
  • Learning to Rank Answers on Large Online QA Collections (214 citations)

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

Hugo Zaragoza mainly focuses on Information retrieval, Web search query, Artificial intelligence, Ranking and Data mining. His Information retrieval study combines topics from a wide range of disciplines, such as Ranking and World Wide Web. As a part of the same scientific family, Hugo Zaragoza mostly works in the field of Web search query, focusing on Semantic search and, on occasion, Social Semantic Web and Semantic Web Stack.

His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Clef and Natural language processing. Hugo Zaragoza studies Learning to rank, a branch of Ranking. His Relevance research incorporates elements of Relevance feedback and Code.

He most often published in these fields:

  • Information retrieval (73.81%)
  • Web search query (29.76%)
  • Artificial intelligence (25.00%)

What were the highlights of his more recent work (between 2008-2018)?

  • Information retrieval (73.81%)
  • Web search query (29.76%)
  • Artificial intelligence (25.00%)

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

Hugo Zaragoza mostly deals with Information retrieval, Web search query, Artificial intelligence, Search engine and Ranking. His study in Information retrieval is interdisciplinary in nature, drawing from both World Wide Web and Data mining. His studies in Web search query integrate themes in fields like Query expansion and Semantic search.

His work carried out in the field of Artificial intelligence brings together such families of science as Ranking, Machine learning and Natural language processing. The various areas that Hugo Zaragoza examines in his Ranking study include Feature, Identification, Factoid, Mean reciprocal rank and SemEval. His biological study spans a wide range of topics, including Probabilistic logic, Divergence-from-randomness model and Relevance feedback.

Between 2008 and 2018, his most popular works were:

  • The Probabilistic Relevance Framework (952 citations)
  • Ad-hoc object retrieval in the web of data (176 citations)
  • Learning to rank answers to non-factoid questions from web collections (152 citations)

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

  • Artificial intelligence
  • Information retrieval
  • Statistics

His main research concerns Information retrieval, Web search query, Artificial intelligence, Web query classification and Ranking. His research on Information retrieval frequently links to adjacent areas such as Data Web. His Web search query research integrates issues from Web standards and Web intelligence, Web modeling.

The Artificial intelligence study combines topics in areas such as Ranking and Machine learning. The Relevance research Hugo Zaragoza does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Binary Independence Model, therefore creating a link between diverse domains of science. His research in Ranking focuses on subjects like Natural language processing, which are connected to Entity linking.

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

The Probabilistic Relevance Framework

Stephen Robertson;Hugo Zaragoza.
(2009)

1472 Citations

Simple BM25 extension to multiple weighted fields

Stephen Robertson;Hugo Zaragoza;Michael Taylor.
conference on information and knowledge management (2004)

703 Citations

Learning to Rank Answers on Large Online QA Collections

Mihai Surdeanu;Massimiliano Ciaramita;Hugo Zaragoza.
meeting of the association for computational linguistics (2008)

283 Citations

Ad-hoc object retrieval in the web of data

Jeffrey Pound;Peter Mika;Hugo Zaragoza.
the web conference (2010)

258 Citations

Microsoft Cambridge at TREC 13: Web and Hard Tracks.

Hugo Zaragoza;Nick Craswell;Michael J. Taylor;Suchi Saria.
text retrieval conference (2004)

224 Citations

Relevance weighting for query independent evidence

Nick Craswell;Stephen Robertson;Hugo Zaragoza;Michael Taylor.
international acm sigir conference on research and development in information retrieval (2005)

204 Citations

Learning to rank answers to non-factoid questions from web collections

Mihai Surdeanu;Massimiliano Ciaramita;Hugo Zaragoza.
Computational Linguistics (2011)

187 Citations

The Perceptron Algorithm with Uneven Margins

Yaoyong Li;Hugo Zaragoza;Ralf Herbrich;John Shawe-Taylor.
international conference on machine learning (2002)

184 Citations

Parsimonious language models for information retrieval

Djoerd Hiemstra;Stephen Robertson;Hugo Zaragoza.
international acm sigir conference on research and development in information retrieval (2004)

155 Citations

Early exit optimizations for additive machine learned ranking systems

B. Barla Cambazoglu;Hugo Zaragoza;Olivier Chapelle;Jiang Chen.
web search and data mining (2010)

137 Citations

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Best Scientists Citing Hugo Zaragoza

Jaap Kamps

Jaap Kamps

University of Amsterdam

Publications: 44

Krisztian Balog

Krisztian Balog

University of Stavanger

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Nick Craswell

Nick Craswell

Microsoft (United States)

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Iadh Ounis

Iadh Ounis

University of Glasgow

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Alessandro Moschitti

Alessandro Moschitti

Amazon (United States)

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Maarten de Rijke

Maarten de Rijke

University of Amsterdam

Publications: 34

Craig Macdonald

Craig Macdonald

University of Glasgow

Publications: 33

W. Bruce Croft

W. Bruce Croft

University of Massachusetts Amherst

Publications: 33

Jianfeng Gao

Jianfeng Gao

Microsoft (United States)

Publications: 28

Yuefeng Li

Yuefeng Li

Queensland University of Technology

Publications: 26

Jimmy Lin

Jimmy Lin

University of Waterloo

Publications: 25

Preslav Nakov

Preslav Nakov

Qatar Computing Research Institute

Publications: 23

Ji-Rong Wen

Ji-Rong Wen

Renmin University of China

Publications: 23

Donald Metzler

Donald Metzler

Google (United States)

Publications: 22

Raffaele Perego

Raffaele Perego

Institute of Information Science and Technologies

Publications: 22

Stephen Robertson

Stephen Robertson

Microsoft (United States)

Publications: 22

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