Explanation-based analysis of taxonomic information in taxonomical text


Explanation-based analysis of taxonomic information in taxonomical text – In this paper, we present an end-to-end algorithm to generate taxonomic descriptions from a corpus. We have two main objectives: (i) to extract the taxonomic units of the information in the query texts and (ii) to generate taxonomical descriptions of the information in taxonomic text that is not available in the data repositories. On the basis of our main goal, we have collected a corpus of query text from three websites: Wikipedia, Wikipedia.com, and Wikidata. The queries contain a large number of information contained in the Wikipedia.com and Wikidata database. The query text comprises a number of different categories, which are then automatically extracted by the algorithm. Using each of them, we have generated more taxonomic descriptions of English taxonomy. This yields an estimate of the taxonomic units of the information in the corpus.

We propose an algorithm for learning sparse representation of an objective function and a sparse representation of a sparse function by exploiting the geometric properties of the manifold space. The resulting algorithm generalizes a widely used approach for convex optimization, which is based on Bayesian networks. Our algorithm is particularly relevant for convex optimization where the manifold space is convex. We give an efficient variant of the method, called the maximum likelihood based convex optimization method (mFBO), and we compare it to other methods, such as the Maximum Mean Discriminant Analysis (LMDA) and Max-Span, which use the manifold space representation objective function to capture the objective in a finite manifold. The optimization loss is $ell_f$ (or $f_alpha$, depending on the manifold) and thus can be computed from a finite set of manifold spaces. We show that the proposed algorithm is not only efficient but also has robustness and convergence guarantees.

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Explanation-based analysis of taxonomic information in taxonomical text

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  • Deep Learning for Large-Scale Data Integration with Label Noise

    An Expectation-Maximization Algorithm for Learning the Geometry of Nonparallel Constraint-O(N) SpacesWe propose an algorithm for learning sparse representation of an objective function and a sparse representation of a sparse function by exploiting the geometric properties of the manifold space. The resulting algorithm generalizes a widely used approach for convex optimization, which is based on Bayesian networks. Our algorithm is particularly relevant for convex optimization where the manifold space is convex. We give an efficient variant of the method, called the maximum likelihood based convex optimization method (mFBO), and we compare it to other methods, such as the Maximum Mean Discriminant Analysis (LMDA) and Max-Span, which use the manifold space representation objective function to capture the objective in a finite manifold. The optimization loss is $ell_f$ (or $f_alpha$, depending on the manifold) and thus can be computed from a finite set of manifold spaces. We show that the proposed algorithm is not only efficient but also has robustness and convergence guarantees.


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