Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: Systems and associated methods for address standardization and applications related thereto are described. Embodiments exploit a common context in a taxonomy and a given address to detect and correct deviations in the address. Embodiments establish a possible path from a root of the taxonomy to a leaf in the taxonomy that can possibly generate a given address. Given a new address, embodiments use complete addresses, and/or segments or elements thereof, to compute the representations of the elements and find a closest matching leaf in the taxonomy. Embodiments then traverse the path to a root node to detect the agreement and disagreement between the path and the address entry. Taxonomical structured is thus used to detect, segregate and standardize the expected fields.

Abstract: Described herein are methods, systems, apparatuses and products for efficient development of a rule-based system. An aspect provides a method including accessing data records; converting said data records to an intermediate form; utilizing intermediate forms to compute similarity scores for said data records; and selecting as an example to be provided for rule making at least one record of said data records having a maximum dissimilarity score indicative of dissimilarity to already considered examples.

Abstract: Described herein are methods, systems, apparatuses and products for efficient development of a rule-based system. An aspect provides a method including accessing data records; converting said data records to an intermediate form; utilizing intermediate forms to compute similarity scores for said data records; and selecting as an example to be provided for rule making at least one record of said data records having a maximum dissimilarity score indicative of dissimilarity to already considered examples.

Abstract: Techniques for identifying one or more inconsistencies between an unstructured document and a back-end fact-base are provided. The techniques include automatically parsing a query document and comparing the document with a back-end fact-base comprising facts relevant to the document, identifying one or more inconsistencies between information mentioned in the document and the facts stored in the back-end fact-base, and providing a response to the query document, wherein the response additionally includes the one or more identified inconsistencies.

Abstract: Described herein are methods, systems, apparatuses and products for efficient development of a rule-based system. An aspect provides a method including accessing data records; converting said data records to an intermediate form; utilizing intermediate forms to compute similarity scores for said data records; and selecting as an example to be provided for rule making at least one record of said data records having a maximum dissimilarity score indicative of dissimilarity to already considered examples.

Abstract: Described herein are methods, systems, apparatuses and products for efficient development of a rule-based system. An aspect provides a method including accessing data records; converting said data records to an intermediate form; utilizing intermediate forms to compute similarity scores for said data records; and selecting as an example to be provided for rule making at least one record of said data records having a maximum dissimilarity score indicative of dissimilarity to already considered examples.

Abstract: Systems and associated methods for address standardization and applications related thereto are described. Embodiments exploit a common context in a taxonomy and a given address to detect and correct deviations in the address. Embodiments establish a possible path from a root of the taxonomy to a leaf in the taxonomy that can possibly generate a given address. Given a new address, embodiments use complete addresses, and/or segments or elements thereof, to compute the representations of the elements and find a closest matching leaf in the taxonomy. Embodiments then traverse the path to a root node to detect the agreement and disagreement between the path and the address entry. Taxonomical structured is thus used to detect, segregate and standardize the expected fields.

Abstract: The present invention provides a hybrid call handling method and system. The method comprises navigating a plurality of received calls from a plurality of callers. The method further comprises monitoring a call health status for each of the plurality of the calls being navigated for entire call duration and notifying a bad call health status of the monitored call to a human agent for employing at least one rectification action. The call health status is determined by monitoring and measuring one or more call parameters. The invention provides for a system for call handling and navigation by an automated system with a human agent assisting the automated system for rectification of calls with bad call health status. Once the call with a bad health is transferred to the human agent, he assists the automated system either by directly communicating with the caller or by communicating using a machine interface.

Abstract: The present invention deals with a method and system for classifying at least one speech-user-utterance in a speech classification system to one of a plurality of pre-defined class types. The method comprises transcribing automatically the at least one speech-user-utterance to obtain at least one automatic-transcribed-text and estimating an estimated-transcribed-text corresponding to the at least one automatic-transcribed-text. The method further comprises classifying the at least one speech-user-utterance based on the estimated-transcribed-text. The estimated-transcribed-text is estimated based on at least one statistical model.

Abstract: A computer implemented method in a language independent system generates audio-driven facial animation given the speech recognition system for just one language. The method is based on the recognition that once alignment is generated, the mapping and the animation hardly have any language dependency in them. Translingual visual speech synthesis can be achieved if the first step of alignment generation can be made speech independent. Given a speech recognition system for a base language, the method synthesizes video with speech of any novel language as the input.