Abstract: Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Abstract: Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Abstract: Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Abstract: A Bell Tree data structure is provided to model the process of chaining the mentions, from one or more documents, into entities, tracking the entire process; where the data structure is used in an entity tracking process that produces multiple results ranked by a product of probability scores.

Abstract: A Bell Tree data structure is provided to model the process of chaining the mentions, from one or more documents, into entities, tracking the entire process; where the data structure is used in an entity tracking process that produces multiple results ranked by a product of probability scores.

Abstract: A Bell Tree data structure is provided to model the process of chaining the mentions, from one or more documents, into entities, tracking the entire process; where the data structure is used in an entity tracking process that produces multiple results ranked by a product of probability scores.

Abstract: An arrangement for adapting statistical parsers to new data using a mathematical transform, particularly a Markov transform. In particular, it is assumed that an initial statistical parser is available and a batch of new data is given. The initial model is mapped to a new model by a Markov matrix, each of whose rows sums to one. In the unsupervised setup, where “true” parses are missing, the transform matrix is obtained by maximizing the log likelihood of the parses of test data decoded using the model before adaptation. The proposed algorithm can be applied to supervised adaptation, as well.

Abstract: A method for automatically updating stock and mutual fund grammars in a speech recognition system includes the step of automatically updating, on a pre-specified basis, a database having a plurality of entries. Each entry respectively corresponds to a publicly traded stock or a publicly traded fund, and respectively includes at least one name of the publicly traded stock or publicly traded fund, a weight for the at least one name, and baseforms of the at least one name. A grammar file for names in the database is automatically updated. The grammar file includes the names and weights for the names. Preferably, the database and grammar file are updated on a daily basis.

Abstract: An arrangement for adapting statistical parsers to new data using a mathematical transform, particularly a Markov transform. In particular, it is assumed that an initial statistical parser is available and a batch of new data is given. The initial model is mapped to a new model by a Markov matrix, each of whose rows sums to one. In the unsupervised setup, where “true” parses are missing, the transform matrix is obtained by maximizing the log likelihood of the parses of test data decoded using the model before adaptation. The proposed algorithm can be applied to supervised adaptation, as well.

Abstract: In accordance with the present invention, a method for providing generation of speech includes the steps of providing input to be acoustically produced, comparing the input to training data or application specific splice files to identify one of words and word sequences corresponding to the input for constructing a phone sequence, using a search algorithm to identify a segment sequence to construct output speech according to the phone sequence and concatenating segments and modifying characteristics of the segments to be substantially equal to requested characteristics. Application specific data is advantageously used to make pertinent information available to synthesize both the phone sequence and the output speech. Also, described is a system for performing operations in accordance with the disclosure.

Abstract: A code signal representing a sequence of code symbols carrying digital information is generated with the frequency components of the code signal being essentially confined to a preselected signalling band lying within the bandwidth of an audio signal within which the code signal is to be embedded. The audio signal is continuously frequency analyzed over a frequency band encompassing the signalling band and the code signal is dynamically filtered as a function of the analysis thereby to provide a modified code signal with frequency components which are, at each time instant, essentially a preselected small proportion of the levels of the corresponding audio signal frequency components. Accordingly, the modified code signal can be combined with the audio signal to obtain a composite audio signal which is not readily distinguishable from the original audio signal by listening.