This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year’s challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output...

Intrinsic motivations play an important role in human learning, particularly in the early stages of childhood development, and ideas from this research field have influenced robotic learning and adaptability. In this paper we investigate one specific type of intrinsic motivation, that of novelty detection and we discuss the reasons that make it a powerful facility for continuous learning. We fo...

Working memory deficits in schizophrenia are key cognitive features of the disorder and have been demonstrated to involve a circuit of brain regions. Here we model this circuitry with a connectionist model representing 3 key processing areas of the circuitry: anterior cingulate gyrus (ACG), dorsal lateral prefrontal cortex (DLPFC), and parietal cortex. The model adapts to a stream of unique inp...

with respect to the process of creation of novelty among economists; that’s why schumpeter has used the term “impossible” in regard to this unknowable process. this is a right term, since this subject matter can’t be incorporated within the theoretical framework determined by orthodox conceptual rationality. considering the importance and necessity of the creation of novelty for the process of ...

We present an attention system that models the dynamics that occur in memory in response to stimuli, which includes habituation, novelty detection, and forgetting. We demonstrate how such an attention system can be used as a trigger for learning perception-action mappings. We discuss the value of social situatedness in the form demonstrator-learner interactions, and show results from both simul...

Novelty Detection techniques might be a promising way of dealing with high-dimensional classification problems in Bioinformatics. This paper presents the early results of the use of a One-Class SVM approach to detect novel classes in two Bioinformatics databases. The results are compatible with the theory and inspire further investigations.