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A well-known drawback in building machine learning semantic relation detectors for natural language is the lack of a large number of qualified training instances for the target relations in multiple languages. Even when good results are achieved, the datasets used by the state-of-the-art approaches are rarely published. In order to address these problems, this work presents an automatic approach to...
We propose a novel algorithm, QuIET, for binary classification of texts. The method automatically generates a set of span queries from a set of annotated documents and uses the query set to categorize unlabeled texts. QuIET generates models that are human understandable. We describe the method and evaluate it empirically against Support Vector Machines, demonstrating a comparable performance for a...
Detection of cyber-attacks is a major responsibility for network managers and security specialists. Most existing Network Intrusion Detection systems rely on inspecting individual packets, an increasingly resource consuming task in today's high speed networks due to the overhead associated with accessing packet content. An alternative approach is to detect attack patterns by investigating IP flows...
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