What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Her primary areas of study are Artificial intelligence, Annotation, Natural language processing, Information retrieval and Data mining.
Her research links Protein function prediction with Artificial intelligence.
Her Annotation study combines topics in areas such as Ontology, Critical Assessment of Function Annotation, Text corpus and Named-entity recognition.
Karin Verspoor has researched Natural language processing in several fields, including Ranking, Scale, Similarity, SemEval and Arabic.
As a part of the same scientific family, Karin Verspoor mostly works in the field of Information retrieval, focusing on Biomedical text mining and, on occasion, Information extraction, Data science, World Wide Web and Probabilistic latent semantic analysis.
Her studies deal with areas such as Multivariate mutual information, Pointwise mutual information, Variation of information, DNA microarray and Test set as well as Data mining.
Her most cited work include:
- A large-scale evaluation of computational protein function prediction (624 citations)
- Findings of the 2016 Conference on Machine Translation (327 citations)
- An expanded evaluation of protein function prediction methods shows an improvement in accuracy (241 citations)
What are the main themes of her work throughout her whole career to date?
Karin Verspoor mainly focuses on Artificial intelligence, Natural language processing, Information retrieval, Data mining and Annotation.
She focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Protein function prediction.
Her Natural language processing study combines topics in areas such as Named-entity recognition, Word, Coreference and Training set.
Her work is dedicated to discovering how Information retrieval, Text mining are connected with Data science and other disciplines.
Her Annotation research is under the purview of Bioinformatics.
Her Information extraction research is multidisciplinary, incorporating elements of Event and Biomedical text mining.
She most often published in these fields:
- Artificial intelligence (38.13%)
- Natural language processing (29.14%)
- Information retrieval (21.58%)
What were the highlights of her more recent work (between 2019-2021)?
- Information retrieval (21.58%)
- Information extraction (10.79%)
- Artificial intelligence (38.13%)
In recent papers she was focusing on the following fields of study:
Information retrieval, Information extraction, Artificial intelligence, Named-entity recognition and Identification are her primary areas of study.
Her work carried out in the field of Information retrieval brings together such families of science as Ranking, Event, Key and Cheminformatics.
The concepts of her Information extraction study are interwoven with issues in Context, Clef, Analytics and Section.
Her biological study spans a wide range of topics, including Domain, Machine learning and Natural language processing.
Her research on Natural language processing often connects related topics like Annotation.
In her study, which falls under the umbrella issue of Named-entity recognition, Training set is strongly linked to Test data.
Between 2019 and 2021, her most popular works were:
- Describing the antimicrobial usage patterns of companion animal veterinary practices; free text analysis of more than 4.4 million consultation records. (9 citations)
- COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research. (8 citations)
- Overview of ChEMU 2020: Named Entity Recognition and Event Extraction of Chemical Reactions from Patents (7 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Karin Verspoor mainly investigates Information extraction, Information retrieval, Artificial intelligence, Data mining and Key.
Her research integrates issues of Domain, Regression, Algorithm, Machine learning and Subclinical infection in her study of Artificial intelligence.
Her study in Domain is interdisciplinary in nature, drawing from both Annotation, Bridging and Anaphora, Natural language processing.
Her study in the fields of Overfitting under the domain of Machine learning overlaps with other disciplines such as Focus.
The Data mining study combines topics in areas such as Genotype imputation, Estimator, Sample size determination and Supplementary data.
Karin Verspoor has included themes like Identification, Event, Event trigger, Cheminformatics and Named-entity recognition in her Key study.
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