What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Information retrieval
His scientific interests lie mostly in Information retrieval, Artificial intelligence, Natural language processing, Language model and Probabilistic logic.
His research brings together the fields of Divergence-from-randomness model and Information retrieval.
His studies deal with areas such as Baseline, Data mining, Search engine, Machine learning and Spelling as well as Artificial intelligence.
His Natural language processing research includes themes of Query language, Query expansion, Multilingualism and Relevance feedback.
The concepts of his Language model study are interwoven with issues in Smoothing, Web page, Meaning, Word and Section.
He has researched Ranking in several fields, including Ranking and Statistical model.
His most cited work include:
- Using Language Models for Information Retrieval (432 citations)
- The Importance of Prior Probabilities for Entry Page Search (264 citations)
- Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002 (191 citations)
What are the main themes of his work throughout his whole career to date?
Djoerd Hiemstra mainly investigates Information retrieval, Artificial intelligence, World Wide Web, Natural language processing and Relevance.
His Human–computer information retrieval, Search engine, Ranking, Query expansion and Query language study are his primary interests in Information retrieval.
The Human–computer information retrieval study combines topics in areas such as Document retrieval, Vector space model, Concept search and Visual Word.
His Search engine research is multidisciplinary, incorporating perspectives in Web page and Federated search.
Djoerd Hiemstra interconnects Web search query, Web query classification and RDF query language in the investigation of issues within Query expansion.
His Artificial intelligence study combines topics in areas such as Set, Machine learning and Data mining.
He most often published in these fields:
- Information retrieval (60.13%)
- Artificial intelligence (24.76%)
- World Wide Web (18.01%)
What were the highlights of his more recent work (between 2014-2021)?
- Information retrieval (60.13%)
- Test (4.18%)
- World Wide Web (18.01%)
In recent papers he was focusing on the following fields of study:
His main research concerns Information retrieval, Test, World Wide Web, Artificial intelligence and Social media.
His research on Information retrieval frequently connects to adjacent areas such as Web crawler.
His work carried out in the field of Test brings together such families of science as Citation, Creativity and Reading.
His Artificial intelligence research includes elements of Construct, Machine learning and Natural language processing.
His work in the fields of Natural language processing, such as Language model, overlaps with other areas such as Social theory and Ethnic group.
His Federated search study combines topics from a wide range of disciplines, such as Human–computer information retrieval and Selection.
Between 2014 and 2021, his most popular works were:
- A cross-benchmark comparison of 87 learning to rank methods (47 citations)
- Evaluation and analysis of term scoring methods for term extraction (26 citations)
- The effects of strength-based versus deficit-based self-regulated learning strategies on students’ effort intentions (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Programming language
Information retrieval, Test, Artificial intelligence, World Wide Web and Naive Bayes classifier are his primary areas of study.
A large part of his Information retrieval studies is devoted to Relevance.
His Artificial intelligence research incorporates themes from Machine learning and Natural language processing.
In general Natural language processing study, his work on Language model often relates to the realm of Social theory and Ethnic group, thereby connecting several areas of interest.
The study incorporates disciplines such as Sample and Extension in addition to World Wide Web.
His work is dedicated to discovering how Naive Bayes classifier, Baseline are connected with Ranking and other disciplines.
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