Abstract: A system and method to query, update, and reduce a size of locking data for a dataset including a fixed plurality of dimensions each having a fixed plurality of members comprising index tables for the dimensions and a locks table specifying one of a fixed plurality of lock states for the plurality of members, where the fixed plurality of members each have an associated index.

Abstract: A system and method to query, update, and reduce a size of locking data for a dataset including a fixed plurality of dimensions each having a fixed plurality of members comprising index tables for the dimensions and a locks table specifying one of a fixed plurality of lock states for the plurality of members, where the fixed plurality of members each have an associated index.

Abstract: An aggregation service aggregates extraction results from diverse named entity recognition (“NER”) services, which can help improve the quality of extracted information. In some cases, the aggregation service considers differences in entity type classifications when aggregating extraction results from different NER services. The aggregation service can also consider performance characteristics (e.g., error rates) for the different NER services. For example, the aggregation service receives extraction results generated for a document corpus according to an entity type schema for each of multiple different NER services, then aggregates the extraction results based at least in part on relations between entity types for the NER services. For a given annotation area, the computing system can identify hypotheses and rank the hypotheses according to an aggregation approach.

Abstract: A system and method to query, update, and reduce a size of locking data for a dataset including a fixed plurality of dimensions each having a fixed plurality of members comprising index tables for the dimensions and a locks table specifying one of a fixed plurality of lock states for the plurality of members, where the fixed plurality of members each have an associated index.

Abstract: An aggregation service aggregates extraction results from diverse named entity recognition (“NER”) services, which can help improve the quality of extracted information. In some cases, the aggregation service considers differences in entity type classifications when aggregating extraction results from different NER services. The aggregation service can also consider performance characteristics (e.g., error rates) for the different NER services. For example, the aggregation service receives extraction results generated for a document corpus according to an entity type schema for each of multiple different NER services, then aggregates the extraction results based at least in part on relations between entity types for the NER services. For a given annotation area, the computing system can identify hypotheses and rank the hypotheses according to an aggregation approach.

Abstract: An aggregation service aggregates extraction results from diverse named entity recognition (“NER”) services, which can help improve the quality of extracted information. In some cases, the aggregation service considers differences in entity type classifications when aggregating extraction results from different NER services. The aggregation service can also consider performance characteristics (e.g., error rates) for the different NER services. For example, the aggregation service receives extraction results generated for a document corpus according to an entity type schema for each of multiple different NER services, then aggregates the extraction results based at least in part on relations between entity types for the NER services. For a given annotation area, the computing system can identify hypotheses and rank the hypotheses according to an aggregation approach.

Abstract: A code generating framework generates code for a model Application Program Interface (API). The framework comprises three components: an API code generator, a serialization code generator, and a deserialization code generator. The API code generator generates code for a model API. This model API produces a first model instance version in a first language. Code from the serialization code generator converts the model instance into a second version in a different language accessible to other applications (e.g., graphical modeling editors). Code from the deserialization code generator converts the second version of the model instance back into the original language. In a particular embodiment, the code generation framework generates JavaScript/XSJS APIs for manipulating model instances compatible with the Eclipse Modeling Framework (EMF).

Abstract: A code generating framework generates code for a model Application Program Interface (API). The framework comprises three components: an API code generator, a serialization code generator, and a deserialization code generator. The API code generator generates code for a model API. This model API produces a first model instance version in a first language. Code from the serialization code generator converts the model instance into a second version in a different language accessible to other applications (e.g., graphical modeling editors). Code from the deserialization code generator converts the second version of the model instance back into the original language. In a particular embodiment, the code generation framework generates JavaScript/XSJS APIs for manipulating model instances compatible with the Eclipse Modeling Framework (EMF).

Abstract: An aggregation service aggregates extraction results from diverse named entity recognition (“NER”) services, which can help improve the quality of extracted information. In some cases, the aggregation service considers differences in entity type classifications when aggregating extraction results from different NER services. The aggregation service can also consider performance characteristics (e.g., error rates) for the different NER services. For example, the aggregation service receives extraction results generated for a document corpus according to an entity type schema for each of multiple different NER services, then aggregates the extraction results based at least in part on relations between entity types for the NER services. For a given annotation area, the computing system can identify hypotheses and rank the hypotheses according to an aggregation approach.

Abstract: A system and method of merging text analysis results. The system uses a set of three corrected, weakened Jaccard factors to determine whether the respective results of multiple text analysis operations are equal, subtypes of each other or associated with each other, in order to merge the results.

Abstract: A system and method of merging text analysis results. The system uses a set of three corrected, weakened Jaccard factors to determine whether the respective results of multiple text analysis operations are equal, subtypes of each other or associated with each other, in order to merge the results.