Abstract: A scalable hierarchical coreference method that employs a homomorphic compression scheme that supports addition and partial subtraction to more efficiently represent the data and the evolving intermediate results of probabilistic inference. The method may encode the features underlying conditional random field models of coreference resolution so that cosine similarities can be efficiently computed. The method may be applied to compressing features and intermediate inference results for conditional random fields. The method may allow compressed representations to be added and subtracted in a way that preserves the cosine similarities.

Abstract: A scalable hierarchical coreference method that employs a homomorphic compression scheme that supports addition and partial subtraction to more efficiently represent the data and the evolving intermediate results of probabilistic inference. The method may encode the features underlying conditional random field models of coreference resolution so that cosine similarities can be efficiently computed. The method may be applied to compressing features and intermediate inference results for conditional random fields. The method may allow compressed representations to be added and subtracted in a way that preserves the cosine similarities.

Abstract: Embodiments perform string similarity analysis by receiving candidate strings of a collection to be searched and transforming each candidate string into one or more features. Embodiments generate a feature index that maps each of the features to one or more candidate strings which include the features, and transform the feature index into a low-memory index by byte encoding a sorted integer array into an encoded byte array. The transforming into a low-memory index further includes hashing each feature to an integer value and storing using a tightly-packed map a hashed value to feature identification (“ID”) mapping, using a first tightly-packed list to map each of the feature IDs to one or more candidate strings which include the features, and using a second tightly-packed list to store the feature IDs.

Abstract: A scalable hierarchical coreference method that employs a homomorphic compression scheme that supports addition and partial subtraction to more efficiently represent the data and the evolving intermediate results of probabilistic inference. The method may encode the features underlying conditional random field models of coreference resolution so that cosine similarities can be efficiently computed. The method may be applied to compressing features and intermediate inference results for conditional random fields. The method may allow compressed representations to be added and subtracted in a way that preserves the cosine similarities.

Abstract: A scalable hierarchical coreference method that employs a homomorphic compression scheme that supports addition and partial subtraction to more efficiently represent the data and the evolving intermediate results of probabilistic inference. The method may encode the features underlying conditional random field models of coreference resolution so that cosine similarities can be efficiently computed. The method may be applied to compressing features and intermediate inference results for conditional random fields. The method may allow compressed representations to be added and subtracted in a way that preserves the cosine similarities.

Abstract: Embodiments perform string similarity analysis by receiving candidate strings of a collection to be searched and transforming each candidate string into one or more features. Embodiments generate a feature index that maps each of the features to one or more candidate strings which include the features, and transform the feature index into a low-memory index by byte encoding a sorted integer array into an encoded byte array. The transforming into a low-memory index further includes hashing each feature to an integer value and storing using a tightly-packed map a hashed value to feature identification (“ID”) mapping, using a first tightly-packed list to map each of the feature IDs to one or more candidate strings which include the features, and using a second tightly-packed list to store the feature IDs.

Abstract: A resource analyzer selects a resource (e.g., document) from a grouping of resources. The grouping of resources can be any type of social tagging system used for information retrieval. The selected resource has an assigned uncontrolled tag and an assigned controlled tag. The controlled tag is a term derived from a controlled vocabulary of terms. Having selected the resource for analyzing, the resource analyzer identifies a first set of resources in the grouping of resources having also been assigned a same value as the uncontrolled tag as the selected resource. Similarly, the resource analyzer identifies a second set of resources in the grouping of resources having also been assigned a same value as the controlled tag. With this information, the resource analyzer then produces a comparison result indicative of a similarity between the first set of resources and the second set of resources.

Abstract: A resource analyzer selects a resource (e.g., document) from a grouping of resources. The grouping of resources can be any type of social tagging system used for information retrieval. The selected resource has an assigned uncontrolled tag and an assigned controlled tag. The controlled tag is a term derived from a controlled vocabulary of terms. Having selected the resource for analyzing, the resource analyzer identifies a first set of resources in the grouping of resources having also been assigned a same value as the uncontrolled tag as the selected resource. Similarly, the resource analyzer identifies a second set of resources in the grouping of resources having also been assigned a same value as the controlled tag. With this information, the resource analyzer then produces a comparison result indicative of a similarity between the first set of resources and the second set of resources.