Bogdan Vrusias

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In general terms the evaluation of a summary depends on how close it is to the chief points in the source text. This begets the question as to what are the chief points in the source text and how is this information used in itself in identifying the source text. This is crucially important when we discuss automatic evaluation of summaries. So the question(More)
Statistical pattern recognition techniques, supervised and unsupervised classification techniques being two good examples here, rely on the computations of similarity and distance metrics. The distances are computed in a multi-dimensional space. The axes of this space in principle relate to the features inherent in the input data. Usually such features are(More)
This paper deals with the problem of semantic transcoding of CCTV video footage. A framework is proposed that combines Computer Vision algorithms that extract visual semantics, together with Natural Language Processing that automatically builds the domain ontology from unstructured text annotations. The final aim is a system that will link the visual and(More)
Automatic text categorization requires the construction of appropriate surrogates for documents within a text collection. The surrogates, often called document vectors, are used to train learning systems for categorising unseen documents. A comparison of different measures (tfidf and weirdness) for creating document vectors is presented together with two(More)