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Titlebook: Advances in Information Retrieval; 29th European Confer Giambattista Amati,Claudio Carpineto,Giovanni Roma Conference proceedings 2007 Spri

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https://doi.org/10.1007/978-3-7091-1841-2defined on graphical domains, rather than on traditional vector spaces. Following the recent developments in statistical relational learning, in this talk, I introduce Diffusion Learning Machines (DLM) whose computation is very much related to Web ranking schemes based on link analysis. Using argume
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Chronischer Schmerz — Therapiekonzepte to automatically recognise stop-words, analyse the tradeoff between efficiency and effectiveness, and compare them with a previous pruning approach. The experiments allow us to conclude that in some situations stop-words pruning is competitive with respect to other inverted file reduction technique
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Chronischer Schmerz — TherapiekonzepteS), Wikis, and backup and archiving solutions. Often, it is desired to enable free-text search over such repositories, i.e. to enable submitting queries that may match any version of any document. We propose an indexing method that takes advantage of the inherent redundancy present in versioned docu
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https://doi.org/10.1007/978-3-642-74316-0t a novel syntactically-based index pruning technique, which uses exclusively shallow syntactic evidence to decide upon which terms to prune. This type of evidence is document-independent, and is based on the assumption that, in a general collection of documents, there exists an approximately propor
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