Abstract: A probabilistic clustering system is defined at least in part by probabilistic model parameters indicative of word counts, ratios, or frequencies characterizing classes of the clustering system. An association of one or more documents in the probabilistic clustering system is changed from one or more source classes to one or more destination classes. Probabilistic model parameters characterizing classes affected by the changed association are locally updated without updating probabilistic model parameters characterizing classes not affected by the changed association.

Abstract: Documents are clustered or categorized to generate a model associating documents with classes. Outlier measures are computed for the documents indicative of how well each document fits into the model. Outlier documents are identified to a user based on the outlier measures and a user selected outlier criterion. Ambiguity measures are computed for the documents indicative of a number of classes with which each document has similarity under the model. If a document is annotated with a label class, a possible corrective label class is identified if the annotated document has higher similarity with the possible corrective label class under the model than with the annotated label class. The clustering or categorizing is repeated adjusted based on received user input to generate an updated model associating documents with classes. Outlier and ambiguity measures are also calculated at runtime for new documents classified using the model.

Abstract: A calibrated categorizer comprises: a multi-class categorizer configured to output class probabilities for an input object corresponding to a set of classes; a class probabilities rescaler configured to rescale class probabilities to generate rescaled class probabilities; and a resealing model learner configured to learn calibration parameters for the class probabilities rescaler based on (i) class probabilities output by the multi-class categorizer for a calibration set of class-labeled objects, (ii) confidence measures output by the multi-class categorizer for the calibration set of class-labeled objects, and (iii) class labels of the calibration set of class-labeled objects, the class probabilities rescaler calibrated by the learned calibration parameters defining a calibrated class probabilities rescaler.

Abstract: In categorizing an object respective to at least two categorization dimensions each defined by a plurality of categories, a probability value indicative of the object is determined for each category of each categorization dimension. A categorization label for the object is selected respective to each categorization dimension based on (i) the determined probability values of the categories of that categorization dimension and (ii) the determined probability values of categories of at least one other of the at least two categorization dimensions.

Abstract: Multiword expressions are mapped to identifiers using finite-state networks. Each of a plurality of multiword expressions is encoded into a regular expression. Each regular expression encodes a base form common to a plurality of derivative forms defined by ones of the multiword expressions. Each of the plurality of regular expressions is compiled with factorization into a set of finite-state networks. A union of the finite-state networks in the set of finite-state networks is performed to define a multiword finite-state network and a set of subnets. The multiword finite-state network and the set of subnets are traversed to identify a path corresponding to one of the plurality of multiword expressions, wherein only transitions originating from the multiword finite-state network are accounted for to ascertain a path number identifying a base form of the one of the plurality of multiword expressions.

Abstract: An imaging system includes a processing component which receives images to be rendered and a rendering device, such as a marking engine, fax machine or email system, in communication with the processing component for rendering an image supplied by the processing component. A haptic interface is in communication with the processing component for inputting commands from the user to the processing component for rendering the image, and outputting feedback from the processing component to the user as a force feedback.

Abstract: Documents are clustered or categorized to generate a model associating documents with classes. Outlier measures are computed for the documents indicative of how well each document fits into the model. Outlier documents are identified to a user based on the outlier measures and a user selected outlier criterion. Ambiguity measures are computed for the documents indicative of a number of classes with which each document has similarity under the model. If a document is annotated with a label class, a possible corrective label class is identified if the annotated document has higher similarity with the possible corrective label class under the model than with the annotated label class. The clustering or categorizing is repeated adjusted based on received user input to generate an updated model associating documents with classes. Outlier and. ambiguity measures are also calculated at runtime for new documents classified using the model.

Abstract: Words of an input string are morphologically analyzed to identify their alternative base forms and parts of speech. The analyzed words of the input string are used to compile the input string into a first finite-state network. The first finite-state network is matched with a second finite-state network of multiword expressions to identify all subpaths of the first finite-state network that match one or more complete paths in the second finite-state network. Each matching subpath of the first finite-state network and path of the second finite-state network identify a multiword expression in the input string. The morphological analysis is performed without disambiguating words and without segmenting the input string into sentences in the input string to compile the first finite-state network with at least one path that identifies alternative base forms or parts of speech of a word in the input string.

Abstract: A probabilistic clustering system is defined at least in part by probabilistic model parameters indicative of word counts, ratios, or frequencies characterizing classes of the clustering system. An association of one or more documents in the probabilistic clustering system is changed from one or more source classes to one or more destination classes. Probabilistic model parameters characterizing classes affected by the changed association are locally updated without updating probabilistic model parameters characterizing classes not affected by the changed association.

Abstract: An imaging system includes a processing component which receives images to be rendered and a rendering device, such as a marking engine, fax machine or email system, in communication with the processing component for rendering an image supplied by the processing component. A haptic interface is in communication with the processing component for inputting commands from the user to the processing component for rendering the image, and outputting feedback from the processing component to the user as a force feedback.

Abstract: In categorizing an object respective to at least two categorization dimensions each defined by a plurality of categories, a probability value indicative of the object is determined for each category of each categorization dimension. A categorization label for the object is selected respective to each categorization dimension based on (i) the determined probability values of the categories of that categorization dimension and (ii) the determined probability values of categories of at least one other of the at least two categorization dimensions.

Abstract: Multiword expressions are mapped to identifiers using finite-state networks. Each of a plurality of multiword expressions is encoded into a regular expression. Each regular expression encodes a base form common to a plurality of derivative forms defined by ones of the multiword expressions. Each of the plurality of regular expressions is compiled with factorization into a set of finite-state networks. A union of the finite-state networks in the set of finite-state networks is performed to define a multiword finite-state network and a set of subnets. The multiword finite-state network and the set of subnets are traversed to identify a path corresponding to one of the plurality of multiword expressions, wherein only transitions originating from the multiword finite-state network are accounted for to ascertain a path number identifying a base form of the one of the plurality of multiword expressions.

Abstract: Words of an input string are morphologically analyzed to identify their alternative base forms and parts of speech. The analyzed words of the input string are used to compile the input string into a first finite-state network. The first finite-state network is matched with a second finite-state network of multiword expressions to identify all subpaths of the first finite-state network that match one or more complete paths in the second finite-state network. Each matching subpath of the first finite-state network and path of the second finite-state network identify a multiword expression in the input string. The morphological analysis is performed without disambiguating words and without segmenting the input string into sentences in the input string to compile the first finite-state network with at least one path that identifies alternative base forms or parts of speech of a word in the input string.