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.
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.
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.
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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The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets.
Damian Szklarczyk;Annika L. Gable;Katerina C. Nastou;David Lyon.
Nucleic Acids Research (2021)
Universal Dependencies v1: A Multilingual Treebank Collection
Joakim Nivre;Marie-Catherine de Marneffe;Filip Ginter;Yoav Goldberg.
language resources and evaluation (2016)
brat: a Web-based Tool for NLP-Assisted Text Annotation
Pontus Stenetorp;Sampo Pyysalo;Goran Topić;Tomoko Ohta.
conference of the european chapter of the association for computational linguistics (2012)
Overview of BioNLP'09 Shared Task on Event Extraction
Jin-Dong Kim;Tomoko Ohta;Sampo Pyysalo;Yoshinobu Kano.
north american chapter of the association for computational linguistics (2009)
BioInfer: a corpus for information extraction in the biomedical domain
Sampo Pyysalo;Filip Ginter;Juho Heimonen;Jari Björne.
BMC Bioinformatics (2007)
Distributional Semantics Resources for Biomedical Text Processing
S Pyysalo;F Ginter;H Moen;T Salakoski.
In: Proceedings of LBM 2013; 2013. p. 39-44. (2013)
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman;Martin Popel;Milan Straka;Jan Hajic.
conference on computational natural language learning (2017)
All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning
Antti Airola;Sampo Pyysalo;Jari Björne;Tapio Pahikkala.
BMC Bioinformatics (2008)
How to Train good Word Embeddings for Biomedical NLP
Billy Chiu;Gamal K. O. Crichton;Anna Korhonen;Sampo Pyysalo.
meeting of the association for computational linguistics (2016)
Comparative analysis of five protein-protein interaction corpora
Sampo Pyysalo;Antti Airola;Juho Heimonen;Jari Björne.
BMC Bioinformatics (2008)
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