Anett Püschel

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UNLABELLED BACKGROUND Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of(More)
This paper describes OCMiner, a high-performance semantic text processing system for large document collections of scientific publications, and its performance regarding chemical named entity recognition in patent texts within the BioCreative V CHEMDNER-Patents challenge which was set up for this purpose. OCMiner permits adjusting the quality of annotation(More)
We present OCMiner, a high-performance text processing system for large document collections of scientific publications. Several linguistic options allow adjusting the quality of annotation results which can be specialized and fine-tuned for the recognition of Life Science terms. Recognized terms are mapped to semantic concepts which are ontologically(More)
We adapted OCMiner, a modular text processing pipeline especially suited for high-speed processing of large document collections, to a specific controlled vocabulary as given by the Comparative Toxicogenomic Database (CTD). We provide a RESTful web service which processes documents given in the BioCreative XML format and annotates them with domainspecific(More)
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