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We have explored and implemented different approaches to named entity recognition in German, a difficult task in this language since both regular nouns and proper names are capitalized. Our goal is to identify and recognise person names, geographical names and company names in a computer magazine corpus. Our geographical name classifier works with(More)
Automatic extraction of biological network information is one of the most desired and most complex tasks in biological text mining. The BioCreative track 4 provides training data and an evaluation environment for the extraction of causal relationships in Biological Expression Language (BEL). BEL is a modeling language that is easily editable by humans or by(More)
This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the inflected form given a lemma and a set of morpho-syntactic features. We focus on neural network approaches that can tackle the task in a limited-resource setting. As the transduction of the lemma into(More)
Our submissions for the GDI 2017 Shared Task are the results from three different types of classifiers: Na¨ıve Bayes, Conditional Random Fields (CRF), and Support Vector Machine (SVM). Our CRF-based run achieves a weighted F1 score of 65% (third rank) being beaten by the best system by 0.9%. Measured by classification accuracy, our ensemble run (Na¨ıve(More)
This paper reports on the annotation and maximum-entropy modeling of the semantics of two German prepositions, mit ('with') and auf ('on'). 500 occurrences of each preposition were sampled from a treebank and annotated with syntacto-semantic classes by two annotators. The classification is guided by a perspective of information extraction, relies on(More)
The BioCreative challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. The biocurator community, as an active user of biomedical literature, provides a diverse and engaged end user group for text mining tools. Earlier BioCreative challenges involved many text mining(More)
Research scientists and companies working in the domains of biomedicine and genomics are increasingly faced with the problem of efficiently locating, within the vast body of published scientific findings, the critical pieces of information that are needed to direct current and future research investment. In this report we describe approaches taken within(More)
BACKGROUND Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To(More)
We describe a system for the detection of mentions of protein-protein interactions in the biomedical scientific literature. The original system was developed as a part of the OntoGene project, which focuses on using advanced computational linguistic techniques for text mining applications in the biomedical domain. In this paper, we focus in particular on(More)