David Luis Wiegandt

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Recently, methods for Chemical Named Entity Recognition (NER) have gained substantial interest, driven by the need for automatically analyzing todays ever growing collections of biomedical text. Chemical NER for patents is particularly essential due to the high economic importance of pharmaceutical findings. However, NER on patents has essentially been(More)
As scientific workflows increasingly gain popularity as a means of automated data analysis, the repositories such workflows are shared in have grown to sizes that require advanced methods for managing the workflows they contain. To facilitate clustering of similar workflows as well as reuse of existing components, a similarity measure for workflows is(More)
Motivation Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local(More)
The accurate identification of gene and protein names in patents is an essential step in many commercially highly relevant applications, such as patent retrieval, prior art search, or patent classification. Since patents exhibit a number of properties that make them quite different from scientific articles, it is questionable whether tools developed for the(More)
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