What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Information retrieval
- The Internet
Ian Soboroff mostly deals with Information retrieval, Track, Relevance, Ranking and Information seeking.
His Information retrieval research integrates issues from Test, Pooling, Artificial intelligence, Set and NIST.
His Test research includes elements of Gold standard and Subject.
The various areas that Ian Soboroff examines in his Artificial intelligence study include Natural language processing, Machine learning and Set.
Ian Soboroff has included themes like Ranking, Intranet and Software deployment in his Ranking study.
His Information seeking study frequently links to other fields, such as Blogosphere.
His most cited work include:
- Overview of the TREC-2005 Enterprise Track (283 citations)
- Overview of the TREC 2009 Web Track (251 citations)
- Overview of the TREC 2006 Blog Track (246 citations)
What are the main themes of his work throughout his whole career to date?
Ian Soboroff mainly focuses on Information retrieval, World Wide Web, Track, NIST and Test.
His study of Relevance is a part of Information retrieval.
His work carried out in the field of World Wide Web brings together such families of science as Text Retrieval Conference and Field.
His studies deal with areas such as Pooling and Data science as well as Test.
His Social media research is multidisciplinary, incorporating perspectives in Privacy preserving and Information privacy, Internet privacy.
Ian Soboroff has researched Data Web in several fields, including Social Semantic Web and Semantic Web Stack.
He most often published in these fields:
- Information retrieval (60.14%)
- World Wide Web (24.48%)
- Track (18.88%)
What were the highlights of his more recent work (between 2018-2021)?
- Data science (10.49%)
- Test (16.08%)
- Information needs (6.29%)
In recent papers he was focusing on the following fields of study:
Ian Soboroff focuses on Data science, Test, Information needs, Pandemic and Scientific literature.
His Data science study combines topics from a wide range of disciplines, such as Domain, Clef, Benchmark and Set.
His Clef study incorporates themes from IR evaluation and Generalizability theory.
His Test study frequently involves adjacent topics like Information retrieval.
By researching both Information retrieval and Overfitting, Ian Soboroff produces research that crosses academic boundaries.
Ian Soboroff interconnects Ground truth, Key, Robustness and Automatic summarization in the investigation of issues within Information needs.
Between 2018 and 2021, his most popular works were:
- TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19. (47 citations)
- TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection. (24 citations)
- TREC Incident Streams: Finding Actionable Information on Social Media (18 citations)
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