Techniques include obtaining, with a computer system, a natural-language-text document comprising unstructured text; generating, with the computer system, based on a first set of machine learning model parameters, a neural representation of the unstructured text; identifying, with the computer system, based on the neural representation, a trigger word located within the unstructured text and associated with a first category; determining, with the computer system, based on the trigger word, a region within the unstructured text comprising descriptors associated with the first category; determining, with the computer system, from the region based on a second set of machine learning model parameters, a descriptor describing an action or condition of the first category; generating, with the computer system, a data model object comprising the descriptor defining an action or condition of the first category; and storing, with the computer system, the data model object in memory.

Systems and methods for displaying hierarchical table headers as disclosed. The systems and methods can include operations performed by a viewer engine. The operations can include detecting a user interaction with a display of a portion of data. The operations can further include determining a second portion of the data to display. The operations can additionally include obtaining data information for the second portion of the data. The data information can include information about headers for the second portion of the data and information about child-parent relationships between the headers. The operations can include determining one or more hierarchical headers for the second portion of the data. The operations can further include rendering a table including the second portion of the data. The operations can additionally include displaying a display depicted the one or more hierarchical headers and a subset of the table including the second portion of the data.

An information processing system receives an evaluation on one evaluation target by a user in an aggregate of evaluation targets, to which results of evaluations are to be individually given, and gives a result of the evaluation to another evaluation target different from the one evaluation target in a case where the one evaluation target is evaluated.

An information processing system receives an evaluation on one evaluation target by a user in an aggregate of evaluation targets, to which results of evaluations are to be individually given, and gives a result of the evaluation to another evaluation target different from the one evaluation target in a case where the one evaluation target is evaluated.

Embodiments include generating automations for a content collaboration system. Generating automations can include displaying graphical objects corresponding to documents hosted by the content collaboration system at a navigation pane of the content collaboration system and determining a deviation metric using a hierarchical structure of the graphical objects and a reference structure. In response to the deviation metric satisfying a criteria, an automation rule for the change to the hierarchical structure of the displayed graphical objects can be displayed. The automation rule can include a reference between a graphical object of the displayed graphical objects and a root reference and a change to the reference between the graphical object and the root reference. Embodiments can also include executing the automation rule to update the hierarchical structure of the displayed graphical objects and update hierarchical structures of the one or more additional graphical objects hosted by the collaboration system.

Embodiments include generating automations for a content collaboration system. Generating automations can include displaying graphical objects corresponding to documents hosted by the content collaboration system at a navigation pane of the content collaboration system and determining a deviation metric using a hierarchical structure of the graphical objects and a reference structure. In response to the deviation metric satisfying a criteria, an automation rule for the change to the hierarchical structure of the displayed graphical objects can be displayed. The automation rule can include a reference between a graphical object of the displayed graphical objects and a root reference and a change to the reference between the graphical object and the root reference. Embodiments can also include executing the automation rule to update the hierarchical structure of the displayed graphical objects and update hierarchical structures of the one or more additional graphical objects hosted by the collaboration system.

Systems and methods are described herein for logging system events within an electronic machine using an event log structured as a collection of tree-like cause and effect graphs. An event to be logged may be received. A new event node may be created within the event log for the received event. One or more existing event nodes within the event log may be identified as having possibly caused the received event. One or more causal links may be created within the event log between the new event node and the one or more identified existing event nodes. The new event node may be stored as an unattached root node in response to not identifying an existing event node that may have caused the received event.

Systems and methods are described herein for logging system events within an electronic machine using an event log structured as a collection of tree-like cause and effect graphs. An event to be logged may be received. A new event node may be created within the event log for the received event. One or more existing event nodes within the event log may be identified as having possibly caused the received event. One or more causal links may be created within the event log between the new event node and the one or more identified existing event nodes. The new event node may be stored as an unattached root node in response to not identifying an existing event node that may have caused the received event.

Disclosed herein are systems, computer-implemented methods, and computer-readable media for dialog modeling. The method includes receiving spoken dialogs annotated to indicate dialog acts and task/subtask information, parsing the spoken dialogs with a hierarchical, parse-based dialog model which operates incrementally from left to right and which only analyzes a preceding dialog context to generate parsed spoken dialogs, and constructing a functional task structure of the parsed spoken dialogs. The method can further either interpret user utterances with the functional task structure of the parsed spoken dialogs or plan system responses to user utterances with the functional task structure of the parsed spoken dialogs. The parse-based dialog model can be a shift-reduce model, a start-complete model, or a connection path model.

Disclosed herein are systems, computer-implemented methods, and computer-readable media for dialog modeling. The method includes receiving spoken dialogs annotated to indicate dialog acts and task/subtask information, parsing the spoken dialogs with a hierarchical, parse-based dialog model which operates incrementally from left to right and which only analyzes a preceding dialog context to generate parsed spoken dialogs, and constructing a functional task structure of the parsed spoken dialogs. The method can further either interpret user utterances with the functional task structure of the parsed spoken dialogs or plan system responses to user utterances with the functional task structure of the parsed spoken dialogs. The parse-based dialog model can be a shift-reduce model, a start-complete model, or a connection path model.