Abstract: In various examples there is a computer-implemented method of database construction. The method comprises storing a knowledge graph comprising nodes connected by edges, each node representing a topic. Accessing a topic type hierarchy comprising a plurality of types of topics, the topic type hierarchy having been computed from a corpus of text documents. One or more text documents are accessed and the method involves labelling a plurality of the nodes with one or more labels, each label denoting a topic type from the topic type hierarchy, by, using a deep language model; or for an individual one of the nodes representing a given topic, searching the accessed text documents for matches to at least one template, the template being a sequence of words and containing the given topic and a placeholder for a topic type; and storing the knowledge graph comprising the plurality of labelled nodes.

Abstract: In various examples there is a computer-implemented method of database construction. The method comprises storing a knowledge graph comprising nodes connected by edges, each node representing a topic. Accessing a topic type hierarchy comprising a plurality of types of topics, the topic type hierarchy having been computed from a corpus of text documents. One or more text documents are accessed and the method involves labelling a plurality of the nodes with one or more labels, each label denoting a topic type from the topic type hierarchy, by, using a deep language model; or for an individual one of the nodes representing a given topic, searching the accessed text documents for matches to at least one template, the template being a sequence of words and containing the given topic and a placeholder for a topic type; and storing the knowledge graph comprising the plurality of labelled nodes.

Abstract: Described herein are techniques for transforming skill ratings for players from one skill rating space (e.g., established according to a model such as TRUESKILL) to another skill rating space. The techniques determine a function that is useable to transform a skill rating in a first skill rating space into a transformed skill rating in a second skill rating space. The function that is useable to transform a skill rating defines an acceptance region associated with the transformed skill rating. The techniques can then use the transformed skill rating and the acceptance region to match a player with other players. A utility function can be used to evaluate different transformation functions and identify an optimal transformation function useable to match players.

Abstract: In a text recognition system that uses a stochastic finite state network to model a document image layout, the computational efficiency of text line decoding is improved. In a typical implementation, the dynamic programming operation that accomplishes decoding uses actual scores computed between two-dimensional (2D) bitmapped character template images and the (2D) bitmapped observed image. Scoring measures the degree of a match between a character template and the observed image. Computation of these actual scores is replaced with the simpler computation of column-based (i.e., one-dimensional) heuristic scores. Because the column-based heuristic scores can be shown to be a true upper bound on actual template-image scores, the heuristic scores are accurate enough to use in place of actual scoring during text line decoding.

Abstract: In a text recognition system, the computational efficiency of a text line image decoding operation is improved by utilizing the characteristic of a graph known as the cut set. The branches of the data structure that represents the image are initially labeled with estimated scores. When estimated scores are used, the decoding operation must perform iteratively on a text line before producing the best path through the data structure. After each iteration, nodes in the best path are re-scored with actual scores. The decoding operation incorporates an operating mode called skip mode.