What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Linguistics
- Natural language processing
Joakim Nivre mainly investigates Parsing, Artificial intelligence, Natural language processing, Dependency grammar and Computational linguistics.
The various areas that Joakim Nivre examines in his Parsing study include Algorithm and Theoretical computer science.
His research in Artificial intelligence intersects with topics in Contrastive linguistics, Applied linguistics, Phrase structure rules and Morphology.
His Natural language processing research is multidisciplinary, relying on both Dependency, Annotation and Speech recognition.
Joakim Nivre has included themes like Data-driven, Graph and Grammar in his Dependency grammar study.
His Computational linguistics research incorporates elements of Quantitative linguistics and Syntax.
His most cited work include:
- MaltParser: A language-independent system for data-driven dependency parsing (680 citations)
- Universal Dependencies v1: A Multilingual Treebank Collection (602 citations)
- The CoNLL 2007 Shared Task on Dependency Parsing (590 citations)
What are the main themes of his work throughout his whole career to date?
Joakim Nivre mainly focuses on Artificial intelligence, Natural language processing, Parsing, Computational linguistics and Dependency grammar.
His Artificial intelligence study frequently involves adjacent topics like Programming language.
His study in Natural language processing is interdisciplinary in nature, drawing from both Linguistics and Speech recognition.
His study ties his expertise on Theoretical computer science together with the subject of Parsing.
His work deals with themes such as German, Quantitative linguistics, Set, Verb and Phrase, which intersect with Computational linguistics.
His Dependency grammar study combines topics from a wide range of disciplines, such as Graph, Algorithm, Machine learning and Word.
He most often published in these fields:
- Artificial intelligence (74.84%)
- Natural language processing (69.35%)
- Parsing (46.77%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (74.84%)
- Natural language processing (69.35%)
- Parsing (46.77%)
In recent papers he was focusing on the following fields of study:
Joakim Nivre focuses on Artificial intelligence, Natural language processing, Parsing, Universal dependencies and Computational linguistics.
Dependency, Treebank, Dependency grammar, Annotation and Word are the primary areas of interest in his Artificial intelligence study.
His work in Dependency grammar addresses subjects such as Pattern recognition, which are connected to disciplines such as Tree structure.
Joakim Nivre combines subjects such as Syntax, Transformer and Text segmentation with his study of Natural language processing.
Joakim Nivre works mostly in the field of Parsing, limiting it down to concerns involving Sentence and, occasionally, Graph.
His biological study spans a wide range of topics, including Artificial neural network, Lexical analysis, Rhetorical question and Chiasmus.
Between 2015 and 2021, his most popular works were:
- Universal Dependencies v1: A Multilingual Treebank Collection (602 citations)
- CoNLL 2018 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies (177 citations)
- CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (115 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Linguistics
- Natural language processing
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Computational linguistics, Dependency and Parsing.
His work in the fields of Artificial intelligence, such as Treebank, Annotation and Character, overlaps with other areas such as Low resource.
The concepts of his Natural language processing study are interwoven with issues in Transformer and Text segmentation.
His Computational linguistics study combines topics in areas such as Artificial neural network, Word, Theoretical computer science and Machine translation.
His Dependency research includes elements of Scheme and Natural language.
His research integrates issues of Sentence and Pipeline in his study of Parsing.
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