What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Information retrieval
- Machine learning
Douglas W. Oard mainly investigates Information retrieval, Artificial intelligence, Natural language processing, Relevance and Human–computer information retrieval.
In his works, Douglas W. Oard undertakes multidisciplinary study on Information retrieval and Term.
His Artificial intelligence research focuses on Clef and how it connects with Fuzzy logic.
His Natural language processing research is mostly focused on the topic Cross-language information retrieval.
As part of one scientific family, Douglas W. Oard deals mainly with the area of Relevance, narrowing it down to issues related to the Test, and often Variety, Formative assessment and Ranking.
His research integrates issues of Document retrieval and Concept search in his study of Human–computer information retrieval.
His most cited work include:
- Implicit Feedback for Recommender Systems (319 citations)
- Cross-Language Information Retrieval. (249 citations)
- A survey of information retrieval and filtering methods (215 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Information retrieval, Artificial intelligence, Natural language processing, World Wide Web and Relevance.
His Information retrieval research incorporates elements of Test and Clef.
As a part of the same scientific family, Douglas W. Oard mostly works in the field of Artificial intelligence, focusing on Query expansion and, on occasion, Query language.
The various areas that Douglas W. Oard examines in his Natural language processing study include Translation, Arabic, Search engine indexing and Multilingualism.
His Machine translation research integrates issues from Information access, Context and Rule-based machine translation.
His work deals with themes such as Document retrieval and Concept search, which intersect with Human–computer information retrieval.
He most often published in these fields:
- Information retrieval (45.15%)
- Artificial intelligence (40.61%)
- Natural language processing (35.15%)
What were the highlights of his more recent work (between 2015-2021)?
- Information retrieval (45.15%)
- Artificial intelligence (40.61%)
- Test (8.79%)
In recent papers he was focusing on the following fields of study:
Douglas W. Oard focuses on Information retrieval, Artificial intelligence, Test, Natural language processing and Clef.
His Information retrieval research is multidisciplinary, incorporating elements of Annotation and Machine translation.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Duality and Pattern recognition.
His Test research is multidisciplinary, incorporating perspectives in Coreference, Information access, Patent application, Privilege and Custodians.
Douglas W. Oard interconnects Entity linking, Traceability and Selection in the investigation of issues within Natural language processing.
Douglas W. Oard combines subjects such as Question answering, Semantic analysis and Mathematics education with his study of Clef.
Between 2015 and 2021, his most popular works were:
- Tangent-CFT: An Embedding Model for Mathematical Formulas (19 citations)
- Characterizing searches for mathematical concepts (10 citations)
- Patent-Related Tasks at NTCIR (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Law
Douglas W. Oard mainly focuses on Information retrieval, Test, Relevance, Entity linking and Artificial intelligence.
His Information retrieval research includes themes of As is and Index.
Douglas W. Oard has included themes like Question answering, Learning to rank and Clef in his Relevance study.
His studies link Natural language processing with Entity linking.
The concepts of his Natural language processing study are interwoven with issues in Query by Example, Linguistic Data Consortium and Coreference.
Many of his studies on Artificial intelligence apply to Pattern recognition as well.
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