What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Programming language
The scientist’s investigation covers issues in Information retrieval, Artificial intelligence, Data mining, Machine learning and Question answering.
His study in the field of Relevance and Search engine is also linked to topics like Forum spam and Filter.
His Relevance research is multidisciplinary, relying on both Ranking and Measure.
Gordon V. Cormack combines subjects such as Construct and Active learning with his study of Artificial intelligence.
His study in the field of Ranking also crosses realms of Software deployment, Function and Recall.
Gordon V. Cormack interconnects Ambiguity, Selection, Redundancy and Multiple choice in the investigation of issues within Question answering.
His most cited work include:
- Novelty and diversity in information retrieval evaluation (757 citations)
- Information Retrieval: Implementing and Evaluating Search Engines (401 citations)
- Reciprocal rank fusion outperforms condorcet and individual rank learning methods (245 citations)
What are the main themes of his work throughout his whole career to date?
Gordon V. Cormack mainly investigates Information retrieval, Artificial intelligence, Recall, Machine learning and Data mining.
His work on Relevance, Ranking and Search engine as part of general Information retrieval study is frequently connected to Track and Relevance feedback, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His work deals with themes such as Sampling, Query expansion, Test and Pooling, which intersect with Relevance.
As a part of the same scientific family, Gordon V. Cormack mostly works in the field of Ranking, focusing on Okapi BM25 and, on occasion, Ranking SVM.
His Artificial intelligence research incorporates themes from Pattern recognition and Natural language processing.
He has included themes like Gold standard and Programming language in his Natural language processing study.
He most often published in these fields:
- Information retrieval (44.23%)
- Artificial intelligence (23.08%)
- Recall (12.18%)
What were the highlights of his more recent work (between 2015-2020)?
- Information retrieval (44.23%)
- Recall (12.18%)
- Relevance (11.54%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Information retrieval, Recall, Relevance, Track and Active learning.
His biological study spans a wide range of topics, including Sentiment analysis, Training set, Property, Social media and Coding.
His Relevance study combines topics in areas such as Test, Pooling, Ideal, Selection and Sampling.
His Active learning study incorporates themes from Search engine, Classifier, Artificial intelligence, Multimedia and Machine learning.
His research investigates the connection between Artificial intelligence and topics such as Natural language processing that intersect with issues in Pattern recognition.
His studies deal with areas such as Time complexity and Residual as well as Machine learning.
Between 2015 and 2020, his most popular works were:
- Engineering Quality and Reliability in Technology-Assisted Review (42 citations)
- Scalability of Continuous Active Learning for Reliable High-Recall Text Classification (37 citations)
- TREC 2016 Total Recall Track Overview. (25 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Programming language
Information retrieval, Recall, Relevance feedback, Relevance and Active learning are his primary areas of study.
In his research, Gordon V. Cormack undertakes multidisciplinary study on Information retrieval and Track.
The Relevance study combines topics in areas such as Test, Ideal, Precision and recall, Property and NIST.
His biological study deals with issues like Contrast, which deal with fields such as Artificial intelligence.
His study looks at the intersection of Active learning and topics like Search engine with Active learning, Interface and Flexibility.
The concepts of his Machine learning study are interwoven with issues in Classifier, Scalability, Data mining and Time complexity.
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