What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Natural language processing
His main research concerns Artificial intelligence, Natural language processing, Biomedical text mining, Annotation and Information retrieval.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Named-entity recognition and Data mining.
Sampo Pyysalo works mostly in the field of Natural language processing, limiting it down to topics relating to Class and, in certain cases, Web application and Text annotation.
The various areas that Sampo Pyysalo examines in his Biomedical text mining study include Event, Information extraction and Distributed computing.
His Annotation research is multidisciplinary, relying on both Controlled vocabulary, Dictionaries as Topic and Parsing.
His study looks at the intersection of Information retrieval and topics like Text mining with Bioinformatics, Relationship extraction and Normalization.
His most cited work include:
- brat: a Web-based Tool for NLP-Assisted Text Annotation (619 citations)
- Universal Dependencies v1: A Multilingual Treebank Collection (602 citations)
- Overview of BioNLP'09 Shared Task on Event Extraction (501 citations)
What are the main themes of his work throughout his whole career to date?
Sampo Pyysalo mostly deals with Artificial intelligence, Natural language processing, Biomedical text mining, Information retrieval and Event.
His research in Artificial intelligence intersects with topics in Domain, Machine learning and Named-entity recognition.
His study in Biomedical text mining is interdisciplinary in nature, drawing from both Semantics, Test set, Representation and Identification.
Sampo Pyysalo combines subjects such as Annotation, Set and Relation with his study of Information retrieval.
In his study, which falls under the umbrella issue of Event, Biomedicine is strongly linked to Data science.
The Treebank and Universal dependencies research Sampo Pyysalo does as part of his general Dependency study is frequently linked to other disciplines of science, such as Dependency graph, therefore creating a link between diverse domains of science.
He most often published in these fields:
- Artificial intelligence (75.33%)
- Natural language processing (67.33%)
- Biomedical text mining (26.67%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (75.33%)
- Natural language processing (67.33%)
- Dependency (18.67%)
In recent papers he was focusing on the following fields of study:
Sampo Pyysalo mainly investigates Artificial intelligence, Natural language processing, Dependency, Universal dependencies and Treebank.
His Artificial intelligence study often links to related topics such as Variation.
His Natural language processing research incorporates elements of Annotation and Named-entity recognition.
His Annotation research is multidisciplinary, incorporating perspectives in Syntax, Layer, Predicate and Text segmentation.
His studies in Named-entity recognition integrate themes in fields like Domain, Context, Named entity and Precision and recall.
His work in Domain tackles topics such as Data science which are related to areas like Ontology.
Between 2017 and 2021, his most popular works were:
- Multilingual is not enough: BERT for Finnish (78 citations)
- Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (56 citations)
- Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches (43 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Sampo Pyysalo spends much of his time researching Artificial intelligence, Natural language processing, Named-entity recognition, Treebank and Dependency.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Recall.
His Natural language processing study combines topics in areas such as Word, Word2vec and Transformer.
His studies deal with areas such as Language model, Deep learning, Dependency grammar and Transfer of learning as well as Named-entity recognition.
Treebank is a subfield of Annotation that Sampo Pyysalo tackles.
His research in Dependency intersects with topics in Layer and Syntax.
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