Abstract: A method incorporates data visualization into conversational interfaces. The method receives a user input specifying a natural language command via a conversational interface. The method analyzes the natural language command to determine the type of question. The method also obtains a user preference for viewing responses based on text and/or visualizations. When the user preference includes visualizations and the type of question is answerable using data visualizations, the method: (i) extracts one or more independent analytic phrases from the natural language command; (ii) queries a database using a set of queries based on the extracted analytic phrases, thereby retrieving a data set; and (iii) generates and displays, in the conversational interface, a response incorporating one or more data visualizations, based on the type of question, using the data set.

Abstract: A computing device receives a user input to specify a natural language command directed to a data source. The device identifies a first keyword, corresponding to a first data field from the data source, in the natural language command. The device identifies a second keyword in the natural language command. The second keyword comprises an adjective that provides context for the first data field. The device generates a visual specification based on the first and second keywords. The visual specification specifies the data source, a plurality of visual variables, and a plurality of data fields from the data source, including the first data field. The visual variables include a first visual variable that specifies filtering of the first data field according to the second keyword. The device generates and displays a data visualization based on the visual specification.

Abstract: A computing device receives a user input to specify a natural language command directed to a data source. The device identifies a first keyword, corresponding to a first data field from the data source, in the natural language command. The device identifies a second keyword in the natural language command. The second keyword comprises an adjective that provides context for the first data field. The device generates a visual specification based on the first and second keywords. The visual specification specifies the data source, a plurality of visual variables, and a plurality of data fields from the data source, including the first data field. The visual variables include a first visual variable that specifies filtering of the first data field according to the second keyword. The device generates and displays a data visualization based on the visual specification.

Abstract: A computing device receives user selection of a data source and a natural language command directed to the data source. The device identifies a first keyword and a second keyword in the natural language command. The first keyword corresponds to a first data field from the data source and the second keyword expresses a limit on a range of data values for the first data field. The device generates a visual specification that specifies the data source, a plurality of visual variables, and a plurality of data fields from the data source, including the first data field. The visual variables determine characteristics of visual marks in a data visualization according to the second keyword, and each of the visual variables is associated with a respective data field of the plurality of data fields. The device generates and displays the data visualization based on the visual specification.

Abstract: A computing device receives user selection of a data source and a natural language command directed to the data source. The device identifies a first keyword and a second keyword in the natural language command. The first keyword corresponds to a first data field from the data source and the second keyword expresses a limit on a range of data values for the first data field. The device generates a visual specification that specifies the data source, a plurality of visual variables, and a plurality of data fields from the data source, including the first data field. The visual variables determine characteristics of visual marks in a data visualization according to the second keyword, and each of the visual variables is associated with a respective data field of the plurality of data fields. The device generates and displays the data visualization based on the visual specification.

Abstract: A computing device displays a data visualization interface and receives user selection of a data source and a natural language command directed to the data source. The device identifies a first keyword and second keyword(s) in the natural language command. The second keyword(s) include adjectives that modify the first keyword. The device generates a visual specification that specifies the data source, a plurality of visual variables, and a plurality of data fields from the data source. Each of the visual variables is associated with a respective data field of the plurality of data fields. The first keyword corresponds to one or more first data fields of the plurality of data fields, and the one or more visual variables are associated with the one or more first data fields according to the one or more second keywords. The device generates and displays a data visualization based on the visual specification.

Abstract: An interactive interface for visualizing results from a search of a corpus of machine-readable documents, each of which is associated with at least one category of a category hierarchy. The interactive interface includes a page object generation component, a book generation component, a page turning component, a cone tree generation component, and a cone tree rotation component. The page object generation component generates for each document of the results a link page and a content page. The link page of each document includes at least a link to categories of a category cone tree, which visually represents the category hierarchy. The content page of each document includes a portion of the document. The book generation component generates a visual representation of the pages for the documents of the results as a book. The book includes a first area for displaying a first link page of a first document of the results and a second area for displaying a first content page of the first document.

Abstract: An interactive interface for creating a search query for a corpus of machine-readable documents, each of which is associated with at least one category of a category hierarchy. The interactive interface includes a cone tree generation component, a query specification component, a begin search component, and a query generation component. The cone tree generation component generates and displays a cone tree representing the category hierarchy. The cone tree represents each category of the category hierarchy as a node having a selection object for indicating inclusion of the category in a one of a first group, and a second group. Each selection object is responsive to a cursor control device. The query specification component generates and displays a query specification object including a first group object and a second group object. Each group object is responsive to the cursor control device and indicates members of the group.

Abstract: An unsupervised method of learning the relationships between words and unspecified topics in documents using a computer is described. The computer represents the relationships between words and unspecified topics via word clusters and association strength values, which can be used later during topical characterization of documents. The computer learns the relationships between words and unspecified topics in an iterative fashion from a set of learning documents. The computer preprocesses the training documents by generating an observed feature vector for each document of the set of training documents and by setting association strengths to initial values. The computer then determines how well the current association strength values predict the topical content of all of the learning documents by generating a cost for each document and summing the individual costs together to generate a total cost. If the total cost is excessive, the association strength values are modified and the total cost recalculated.

Abstract: An iterative method of determining the topical content of a document using a computer. The processing unit of the computer determines the topical content of documents presented to it in machine readable form using information stored in computer memory. That information includes word-clusters, a lexicon, and association strength values. The processing unit beings by generating an observed feature vector for the document being characterized, which indicates which of the words of the lexicon appear in the document. Afterward, the processing unit makes an initial prediction of the topical content of the document in the form of a topic belief vector. The processing unit uses the topic belief vector and the association strength values to predict which words of the lexicon should appear in the document. This prediction is represented via a predicted feature vector.