What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Natural language processing
- Semantics
Sameer Pradhan focuses on Artificial intelligence, Natural language processing, SemEval, Semantic role labeling and Information retrieval.
Sameer Pradhan integrates Artificial intelligence with Set in his study.
His Natural language processing study often links to related topics such as Algorithm.
His Word-sense induction study in the realm of SemEval connects with subjects such as Sample.
His Semantic role labeling research is multidisciplinary, incorporating elements of Annotation and Coreference.
His studies deal with areas such as Text corpus, Recall, Task analysis and Test set as well as Information retrieval.
His most cited work include:
- CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes (463 citations)
- Shallow Semantic Parsing using Support Vector Machines. (343 citations)
- SemEval-2007 Task-17: English Lexical Sample, SRL and All Words (268 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Natural language processing, Parsing, eHealth and Semantic role labeling.
He conducts interdisciplinary study in the fields of Artificial intelligence and Set through his works.
His PropBank study in the realm of Natural language processing interacts with subjects such as Structure.
His PropBank study incorporates themes from Question answering and Automatic summarization.
His research investigates the link between Parsing and topics such as Speech recognition that cross with problems in SemEval and Boosting.
His work in the fields of Information retrieval, such as Information extraction, overlaps with other areas such as Key, Measure and Reference implementation.
He most often published in these fields:
- Artificial intelligence (83.05%)
- Natural language processing (77.97%)
- Parsing (25.42%)
What were the highlights of his more recent work (between 2014-2020)?
- Artificial intelligence (83.05%)
- Natural language processing (77.97%)
- Parsing (25.42%)
In recent papers he was focusing on the following fields of study:
Sameer Pradhan mainly focuses on Artificial intelligence, Natural language processing, Parsing, Annotation and PropBank.
In his works, Sameer Pradhan conducts interdisciplinary research on Artificial intelligence and Recursive descent parser.
His study connects Algorithm and Natural language processing.
His work carried out in the field of Parsing brings together such families of science as Speech recognition and Natural language.
His PropBank study combines topics from a wide range of disciplines, such as XML and Syntax.
The Treebank study combines topics in areas such as Predicate, External Data Representation and Coreference.
Between 2014 and 2020, his most popular works were:
- A Transition-based Algorithm for AMR Parsing (130 citations)
- The CoNLL-2015 Shared Task on Shallow Discourse Parsing (109 citations)
- Evaluating the state of the art in disorder recognition and normalization of the clinical narrative. (93 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Natural language processing
- Machine learning
His primary areas of investigation include Artificial intelligence, Natural language processing, Parsing, Natural language and Protocol.
His work on Parser combinator is typically connected to Information system as part of general Natural language processing study, connecting several disciplines of science.
His Parser combinator research is multidisciplinary, incorporating perspectives in Sentence and Top-down parsing.
His study on Information system is intertwined with other disciplines of science such as Named-entity recognition, Unified Medical Language System, Information retrieval, Vocabulary and Information extraction.
The various areas that Sameer Pradhan examines in his Named-entity recognition study include Controlled vocabulary, Normalization and F1 score.
Sameer Pradhan has researched Test set in several fields, including Discourse relation and Benchmark.
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