What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Information retrieval
- Natural language processing
Alan R. Aronson mainly focuses on Unified Medical Language System, Information retrieval, Artificial intelligence, Natural language processing and Search engine indexing.
His Unified Medical Language System research includes themes of Controlled vocabulary, Query language, Query expansion, Biomedical text and Data science.
His Biomedical text study incorporates themes from Named-entity recognition and Knowledge extraction.
Alan R. Aronson undertakes interdisciplinary study in the fields of Information retrieval and National library through his research.
His research investigates the connection between Artificial intelligence and topics such as Thesaurus that intersect with problems in Decision support system.
His work carried out in the field of Search engine indexing brings together such families of science as Text mining and Index.
His most cited work include:
- Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program (1597 citations)
- An overview of MetaMap: historical perspective and recent advances (1045 citations)
- Generating hypotheses by discovering implicit associations in the literature: a case report of a search for new potential therapeutic uses for thalidomide. (176 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Information retrieval, Artificial intelligence, Natural language processing, Unified Medical Language System and Search engine indexing.
As part of the same scientific family, he usually focuses on Information retrieval, concentrating on Annotation and intersecting with Information needs.
His Artificial intelligence research is multidisciplinary, relying on both Text mining and Machine learning.
In his research, Data set is intimately related to Word-sense disambiguation, which falls under the overarching field of Natural language processing.
His Unified Medical Language System study also includes
- Semantics that intertwine with fields like Syntax,
- Thesaurus most often made with reference to Controlled vocabulary.
His work on Indexer and Automatic indexing as part of his general Search engine indexing study is frequently connected to Vocabulary, thereby bridging the divide between different branches of science.
He most often published in these fields:
- Information retrieval (75.95%)
- Artificial intelligence (56.96%)
- Natural language processing (53.16%)
What were the highlights of his more recent work (between 2012-2019)?
- Search engine indexing (39.24%)
- Information retrieval (75.95%)
- Artificial intelligence (56.96%)
In recent papers he was focusing on the following fields of study:
Alan R. Aronson mostly deals with Search engine indexing, Information retrieval, Artificial intelligence, Indexer and Unified Medical Language System.
Alan R. Aronson combines subjects such as Relationship extraction and Subject with his study of Search engine indexing.
His Information retrieval study combines topics in areas such as Text mining and Data mining.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Natural language processing.
His work on Biomedical text as part of general Natural language processing research is often related to Java, thus linking different fields of science.
The Unified Medical Language System study combines topics in areas such as Pairwise comparison and Relevance.
Between 2012 and 2019, his most popular works were:
- MetaMap Lite: an evaluation of a new Java implementation of MetaMap (91 citations)
- The NLM Medical Text Indexer System for Indexing Biomedical Literature. (62 citations)
- Recent Enhancements to the NLM Medical Text Indexer. (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Information retrieval
- Machine learning
His main research concerns Search engine indexing, Indexer, Vocabulary, Information retrieval and Data science.
His study on Vocabulary is intertwined with other disciplines of science such as Ambiguity, Commit, Pace, F1 score and Subject.
His work in the fields of Information retrieval, such as Ranking, intersects with other areas such as Workload.
His Data science research incorporates elements of World Wide Web, Index and Workflow.
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