What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
His primary areas of investigation include Natural language processing, Artificial intelligence, Programming language, Program comprehension and Natural language.
The Natural language processing study combines topics in areas such as Entropy, Transformation based learning, Semantics and Cluster analysis.
His study in the field of Lexical item is also linked to topics like Position.
His Program comprehension research includes elements of Java, Software development, Software construction and Heuristics.
The concepts of his Natural language study are interwoven with issues in Software, Info URI scheme and Source code.
His work on Software maintenance as part of his general Software study is frequently connected to Contextual advertising, thereby bridging the divide between different branches of science.
His most cited work include:
- Towards automatically generating summary comments for Java methods (324 citations)
- CHARACTERIZING STRUCTURAL DESCRIPTIONS PRODUCED BY VARIOUS GRAMMATICAL FORMALISMS (242 citations)
- The equivalence of four extensions of context-free grammars (202 citations)
What are the main themes of his work throughout his whole career to date?
K. Vijay-Shanker mainly investigates Artificial intelligence, Natural language processing, Programming language, Information retrieval and Rule-based machine translation.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Set and Biomedical text mining.
His Natural language processing study integrates concerns from other disciplines, such as Transformation based learning and Program comprehension.
The Programming language study which covers Natural language that intersects with Source code, Software, Software maintenance, Program analysis and Identifier.
His work on Information extraction as part of general Information retrieval research is frequently linked to MEDLINE, bridging the gap between disciplines.
His work carried out in the field of Rule-based machine translation brings together such families of science as Theoretical computer science, Tree, Locality, Grammar and Formalism.
He most often published in these fields:
- Artificial intelligence (48.70%)
- Natural language processing (43.51%)
- Programming language (18.83%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (48.70%)
- Text mining (10.39%)
- Relationship extraction (7.79%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Text mining, Relationship extraction, Natural language processing and Information retrieval are his primary areas of study.
His study looks at the relationship between Artificial intelligence and topics such as Machine learning, which overlap with Adversarial system.
He combines subjects such as Scalability, Computational biology and Pipeline with his study of Text mining.
His primary area of study in Natural language processing is in the field of Natural language.
K. Vijay-Shanker has researched Natural language in several fields, including Program comprehension, Software development, Oracle, Identifier and Empirical research.
His Information retrieval study combines topics in areas such as Sentence, World Wide Web, Interoperability, Knowledge extraction and Semantics.
Between 2013 and 2021, his most popular works were:
- iPTMnet: an integrated resource for protein post-translational modification network discovery. (42 citations)
- miRTex: A Text Mining System for miRNA-Gene Relation Extraction. (40 citations)
- Transcriptome response to heat stress in a chicken hepatocellular carcinoma cell line (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
K. Vijay-Shanker focuses on Computational biology, Text mining, Information retrieval, Knowledge extraction and Posttranslational modification.
His research integrates issues of Relationship extraction and Gene in his study of Text mining.
His studies deal with areas such as XML, World Wide Web and Interoperability as well as Information retrieval.
Extramural combines with fields such as Natural language processing and Artificial intelligence in his investigation.
As a part of the same scientific family, he mostly works in the field of Natural language processing, focusing on Java and, on occasion, Natural language.
His Artificial intelligence study incorporates themes from Programming language, Software and Open source.
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