Evaluating Citation Functions in CiTO: Cognitive Issues

Abstract. Networks of citations are a key tool for referencing, dissem-inating and evaluating research results. The task of characterising the functional role of citations in scientific literature is very difficult, not only for software agents but for humans, too. The main problem is that the mental models of different annotators hardly ever converge to a single shared opinion. The goal of this paper is to investigate how an existing reference model for classifying citations, namely CiTO (Citation Typing Ontology), is interpreted and used by annotators of scientific literature. We present an experiment capturing the cognitive processes behind sub-jects ’ decisions in annotating papers with CiTO, and we provide initial ideas to refine future releases of CiTO

Evaluating Citation Functions in CiTO: Cognitive Issues.

Abstract. Networks of citations are a key tool for referencing, dissem-inating and evaluating research results. The task of characterising the functional role of citations in scientific literature is very difficult, not only for software agents but for humans, too. The main problem is that the mental models of different annotators hardly ever converge to a single shared opinion. The goal of this paper is to investigate how an existing reference model for classifying citations, namely CiTO (Citation Typing Ontology), is interpreted and used by annotators of scientific literature. We present an experiment capturing the cognitive processes behind sub-jects ’ decisions in annotating papers with CiTO, and we provide initial ideas to refine future releases of CiTO

Товарищ Терентий. 1925. № 19

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Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel MR Images

Abstract. A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D MR images. It builds on the discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. Our method uses multi-channel MR intensities (T1, T2, Flair), spatial prior and long-range comparisons with 3D regions to discriminate lesions. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the data is carried out on publicly available labeled cases from the MS Lesion Segmentation Challenge 2008 dataset and demonstrates improved results over the state of the art.