Abstract: A system and method for disambiguating references to entities in a document. In one embodiment, an iterative process is used to disambiguate references to entities in documents. An initial model is used to identify documents referring to an entity based on features contained in those documents. The occurrence of various features in these documents is measured. From the number occurrences of features in these documents, a second model is constructed. The second model is used to identify documents referring to the entity based on features contained in the documents. The process can be repeated, iteratively identifying documents referring to the entity and improving subsequent models based on those identifications. Additional features of the entity can be extracted from documents identified as referring to the entity.

Abstract: A system and method for disambiguating references to entities in a document. In one embodiment, an iterative process is used to disambiguate references to entities in documents. An initial model is used to identify documents referring to an entity based on features contained in those documents. The occurrence of various features in these documents is measured. From the number occurrences of features in these documents, a second model is constructed. The second model is used to identify documents referring to the entity based on features contained in the documents. The process can be repeated, iteratively identifying documents referring to the entity and improving subsequent models based on those identifications. Additional features of the entity can be extracted from documents identified as referring to the entity.

Abstract: A system and method for disambiguating references to entities in a document. In one embodiment, an iterative process is used to disambiguate references to entities in documents. An initial model is used to identify documents referring to an entity based on features contained in those documents. The occurrence of various features in these documents is measured. From the number occurrences of features in these documents, a second model is constructed. The second model is used to identify documents referring to the entity based on features contained in the documents. The process can be repeated, iteratively identifying documents referring to the entity and improving subsequent models based on those identifications. Additional features of the entity can be extracted from documents identified as referring to the entity.

Abstract: A system and method for disambiguating references to entities in a document. In one embodiment, an iterative process is used to disambiguate references to entities in documents. An initial model is used to identify documents referring to an entity based on features contained in those documents. The occurrence of various features in these documents is measured. From the number occurrences of features in these documents, a second model is constructed. The second model is used to identify documents referring to the entity based on features contained in the documents. The process can be repeated, iteratively identifying documents referring to the entity and improving subsequent models based on those identifications. Additional features of the entity can be extracted from documents identified as referring to the entity.

Abstract: A system and method for disambiguating references to entities in a document. In one embodiment, an iterative process is used to disambiguate references to entities in documents. An initial model is used to identify documents referring to an entity based on features contained in those documents. The occurrence of various features in these documents is measured. From the number occurrences of features in these documents, a second model is constructed. The second model is used to identify documents referring to the entity based on features contained in the documents. The process can be repeated, iteratively identifying documents referring to the entity and improving subsequent models based on those identifications. Additional features of the entity can be extracted from documents identified as referring to the entity.

Abstract: A system and method for disambiguating references to entities in a document. In one embodiment, an iterative process is used to disambiguate references to entities in documents. An initial model is used to identify documents referring to an entity based on features contained in those documents. The occurrence of various features in these documents is measured. From the number occurrences of features in these documents, a second model is constructed. The second model is used to identify documents referring to the entity based on features contained in the documents. The process can be repeated, iteratively identifying documents referring to the entity and improving subsequent models based on those identifications. Additional features of the entity can be extracted from documents identified as referring to the entity.

Abstract: A fact repository stores objects. Each object includes a collection of facts, where a fact comprises an attribute and a value. A set of objects from the fact repository are designated for analysis. The presentation engine presents the facts of the objects in a user interface (UI) having a table. Through manipulation of the UI, an end-user can add or remove facts from the table, and sort the table based on the values of particular facts. The presentation engine also presents the facts of the objects in a UI having a graph. Through manipulation of the UI, the end-user can add or remove facts from the graph, and can sort the facts shown in the graph based on values that are shown, or not shown, in the graph. The presentation engine can further present the facts of the objects in UIs including maps and timelines.

Abstract: A presentation engine presents facts in a UI having a graph. Through manipulation of the UI, an end-user can add or remove facts from the graph, and can sort the facts shown in the graph based on values that are shown, or not shown, in the graph. The presentation engine determines a graph type that best facilitates interpretation of the facts by the end-user and automatically creates a graph of that type. Possible graph types include bar graphs, scatter plots, timelines, and maps.

Abstract: A fact repository stores objects. Each object includes a collection of facts, where a fact comprises an attribute and a value. A set of objects from the fact repository are designated for analysis. The presentation engine presents the facts of the objects in a user interface (UI) having a table. Through manipulation of the UI, an end-user can add or remove facts from the table, and sort the table based on the values of particular facts. The presentation engine also presents the facts of the objects in a UI having a graph. Through manipulation of the UI, the end-user can add or remove facts from the graph, and can sort the facts shown in the graph based on values that are shown, or not shown, in the graph. The presentation engine can further present the facts of the objects in UIs including maps and timelines.

Abstract: A fact repository stores objects. Each object includes a collection of facts, where a fact comprises an attribute and a value. An object access module receives objects from the fact repository. The objects can result from multiple different queries executed against the fact repository. A user interface (UI) generation module provides a UI enabling an end-user to designate objects from multiple different queries for subsequent analysis by storing the objects in a virtual collection.

Abstract: A computer-implemented method for providing a candidate list of alternatives for a text selection containing text from multiple input sources, each of which can be stochastic (such as a speech recognition unit, handwriting recognition unit, or input method editor) or non-stochastic (such as a keyboard and mouse). A text component of the text selection may be the result of data processed through a series of stochastic input sources, such as speech input that is converted to text by a speech recognition unit before being used as input into an input method editor. To determine alternatives for the text selection, a stochastic input combiner parses the text selection into text components from different input sources. For each stochastic text component, the combiner retrieves a stochastic model containing alternatives for the text component.