What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Gene
Ulf Leser focuses on Artificial intelligence, Information retrieval, Data mining, Text mining and Query language.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Natural language processing.
In the subject of general Information retrieval, his work in Semantic search is often linked to Scientific literature, thereby combining diverse domains of study.
Ulf Leser has researched Data mining in several fields, including Functional annotation, Protein function prediction, UniProt and Protein–protein interaction.
His studies deal with areas such as Information extraction, World Wide Web, Programming paradigm and Data warehouse as well as Text mining.
His Query language research includes elements of Query expansion, Web query classification, RDF query language, Sargable and Query optimization.
His most cited work include:
- Querying distributed RDF data sources with SPARQL (400 citations)
- The Stratosphere platform for big data analytics (357 citations)
- Deep learning with word embeddings improves biomedical named entity recognition. (261 citations)
What are the main themes of his work throughout his whole career to date?
Ulf Leser spends much of his time researching Artificial intelligence, Information retrieval, Data mining, Natural language processing and Data science.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning, Named-entity recognition and Pattern recognition.
His Information retrieval research is multidisciplinary, relying on both Annotation and Software.
Ulf Leser works mostly in the field of Data mining, limiting it down to concerns involving Set and, occasionally, Theoretical computer science.
His Natural language processing research incorporates themes from Query language and Normalization.
He has included themes like Domain, Data integration and Workflow in his Data science study.
He most often published in these fields:
- Artificial intelligence (21.54%)
- Information retrieval (18.46%)
- Data mining (16.54%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (21.54%)
- Natural language processing (14.62%)
- Artificial neural network (2.31%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Artificial neural network, Cancer research and Named-entity recognition.
He studies Artificial intelligence, namely Deep learning.
As part of the same scientific family, Ulf Leser usually focuses on Natural language processing, concentrating on German and intersecting with Clef, Information extraction and Consistency.
His Named-entity recognition research is multidisciplinary, incorporating perspectives in Cover, Annotation and State.
Ulf Leser interconnects Computation, Data mining, Constraint and Heuristic in the investigation of issues within Set.
Biomedical information and Information retrieval are two areas of study in which Ulf Leser engages in interdisciplinary work.
Between 2018 and 2021, his most popular works were:
- Predictive performance modeling for distributed batch processing using black box monitoring and machine learning (24 citations)
- NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language (20 citations)
- HUNER: improving biomedical NER with pretraining. (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Gene
His primary areas of study are Artificial intelligence, Natural language processing, Medical physics, Precision oncology and Named-entity recognition.
The Artificial intelligence study combines topics in areas such as Batch processing, Machine learning and Scheduling.
His research in Natural language processing intersects with topics in Artificial neural network, Clef, German and Ambiguity.
His Medical physics research is multidisciplinary, incorporating elements of Document retrieval, Document classification, Search engine and MEDLINE.
His work in Named-entity recognition tackles topics such as Language model which are related to areas like Set.
His research investigates the connection with Set and areas like Algorithm which intersect with concerns in Use case.
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