What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Natural language processing
Jerry R. Hobbs mostly deals with Artificial intelligence, Natural language processing, Natural language, Parsing and Coherence.
His Artificial intelligence research includes elements of Event and Metonymy.
His study in Natural language processing is interdisciplinary in nature, drawing from both Semantics, Nondeterministic algorithm and Verb.
His biological study spans a wide range of topics, including Pragmatics and Abductive reasoning.
The Coherence study combines topics in areas such as Discourse Processes, Language research, Discourse analysis and Psychoanalysis.
Jerry R. Hobbs combines subjects such as Key and World Wide Web with his study of Information retrieval.
His most cited work include:
- DAML-S: semantic markup for web services (2450 citations)
- DAML-S: Web Service Description for the Semantic Web (868 citations)
- Interpretation as abduction (808 citations)
What are the main themes of his work throughout his whole career to date?
Jerry R. Hobbs mainly investigates Artificial intelligence, Natural language processing, Natural language, Cognitive science and Commonsense knowledge.
His research in Artificial intelligence intersects with topics in Structure and Granularity.
His work carried out in the field of Natural language processing brings together such families of science as Semantics, Abductive reasoning and Pragmatics.
His Natural language study integrates concerns from other disciplines, such as Computational linguistics, Phenomenon, Reading, Question answering and Knowledge representation and reasoning.
Jerry R. Hobbs interconnects Causality and Metaphor in the investigation of issues within Cognitive science.
The concepts of his Commonsense knowledge study are interwoven with issues in Lexical semantics and Commonsense reasoning.
He most often published in these fields:
- Artificial intelligence (52.84%)
- Natural language processing (43.75%)
- Natural language (25.57%)
What were the highlights of his more recent work (between 2009-2019)?
- Artificial intelligence (52.84%)
- Natural language processing (43.75%)
- Folk psychology (8.52%)
In recent papers he was focusing on the following fields of study:
Jerry R. Hobbs mainly focuses on Artificial intelligence, Natural language processing, Folk psychology, Cognitive science and Natural language.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Event, Pipeline and Set.
Specifically, his work in Natural language processing is concerned with the study of WordNet.
His studies deal with areas such as Theory, Basic science, Lexical semantics, Commonsense reasoning and Set as well as Folk psychology.
His Cognitive science research integrates issues from Interpretation, Theoretical psychology, Mind–body problem, Structure and Notation.
His Natural language research is multidisciplinary, incorporating elements of Parsing, Sentence, Granularity, Lisp and Metalanguage.
Between 2009 and 2019, his most popular works were:
- Making Computational Sense of Montague's Intensional Logic (49 citations)
- Abductive reasoning with a large knowledge base for discourse processing (33 citations)
- Implementing weighted abduction in Markov logic (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Natural language processing
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Natural language, Folk psychology and Lexical semantics.
Artificial intelligence connects with themes related to Pipeline in his study.
Jerry R. Hobbs performs multidisciplinary study in the fields of Natural language processing and Observable via his papers.
His Natural language course of study focuses on Granularity and Question answering, Information retrieval, Automatic summarization and Phenomenon.
Jerry R. Hobbs works mostly in the field of Folk psychology, limiting it down to topics relating to Commonsense reasoning and, in certain cases, Commonsense knowledge and Context.
His Abductive reasoning study incorporates themes from Semantic role labeling and Logical form.
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