Some aspects of the disclosure provide a method for sentence conversion. The method includes receiving a first sentence that is inputted by a user, inputting the first sentence into a first sentence based rewrite model to obtain a second sentence having a same semantic as the first sentence but a different style from the first sentence. The first sentence based rewrite model converts the first sentence into the second sentence without partitioning the first sentence into smaller portions. The method also includes displaying the second sentence. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated

A method and system for displaying an image in a communication thread accessible to a first and second user are disclosed, including receiving an indication of a selection of a first image by the first user for display in the communication thread, analyzing user attribute settings of the first user or the second user to determine if at least one of the first user or the second user has a user attribute setting corresponding to a customizable aspect of the selected first image, and causing, based on the analyzing, in different first and second modes, one of the selected first image or a modified image based on the selected first image to be displayed in the communication thread.

Systems, methods, and products for auto tagging structured PDF documents that do not have accessibility tags. In one embodiment, structured PDF documents having accessibility tags are first parsed and analyzed to organize the visual components of the documents. The relationships of the identified objects to DOM elements (e.g., tags) are determined, and the objects and related DOM elements are stored in training files. The training files are used to train various classifiers. Untagged PDF documents are then parsed to identify included visual objects, and the classifiers are used to determine DOM elements that should be associated with visual objects identified in the untagged PDF documents. This information is used to construct a DOM structure corresponding to each untagged document. A new PDF is then generated corresponding to each untagged document using the generated DOM structure and visual object information.

Systems, methods, and products for auto tagging structured PDF documents that do not have accessibility tags. In one embodiment, structured PDF documents having accessibility tags are first parsed and analyzed to organize the visual components of the documents. The relationships of the identified objects to DOM elements (e.g., tags) are determined, and the objects and related DOM elements are stored in training files. The training files are used to train various classifiers. Untagged PDF documents are then parsed to identify included visual objects, and the classifiers are used to determine DOM elements that should be associated with visual objects identified in the untagged PDF documents. This information is used to construct a DOM structure corresponding to each untagged document. A new PDF is then generated corresponding to each untagged document using the generated DOM structure and visual object information.

The invention relates to computer-implemented method for supplementing a data governance framework with one or more new data governance technical rules. The method comprises providing a plurality of expressions and a first mapping. The expressions assign natural language patterns to technical language patterns. The first mapping maps first terms to data sources. A rule generator receives a new natural language (NL) rule comprising one or more natural-language patterns and one or more first terms. The rule generator resolves the new NL rule into one or more new technical rules interpretable by a respective rule engine and stores the one or more technical rules in a rule repository.

In one aspect there is provided a method. The method may include generating a graphical representation of a decision logic underlying a solution, the graphical representation having a plurality of nodes. A component archetype can be identified. The identified component archetype can support generating a function implementing one of the plurality of nodes in the graphical representation of the solution. An instance of the component can be generated based at least on the component archetype. The function can be generated by invoking the instance of the component. The generated function can be hosted by the instance of the component. Alternately and/or additionally, the generation function can be copied into one or more separate execution environments. Systems and articles of manufacture, including computer program products, are also provided.

Examples are disclosed herein that relate to string transformation sub-program suggestion. One example provides, at a computing device, a method comprising receiving a dataset having a plurality of input strings; receiving a declarative user input associated with a desired string transformation sub-program to be performed using the dataset as input; in a search space having a plurality of string transformation sub-programs, reducing the search space based on (i) the user input and (ii) one or more of the plurality of input strings of the dataset, to thereby form a reduced search space, the reduced search space having at least one string transformation sub-program that is configured to transform one or more of the plurality of input strings of the dataset; and selecting, from the reduced search space, one or more suggested string transformation sub-programs.

Examples are disclosed herein that relate to string transformation sub-program suggestion. One example provides, at a computing device, a method comprising receiving a dataset having a plurality of input strings; receiving a declarative user input associated with a desired string transformation sub-program to be performed using the dataset as input; in a search space having a plurality of string transformation sub-programs, reducing the search space based on (i) the user input and (ii) one or more of the plurality of input strings of the dataset, to thereby form a reduced search space, the reduced search space having at least one string transformation sub-program that is configured to transform one or more of the plurality of input strings of the dataset; and selecting, from the reduced search space, one or more suggested string transformation sub-programs.

Rules are automatically learned via machine-learning techniques to deduce the semantic roles of extracted information elements, as well as, compute the respective levels of certainty that the semantic roles are indeed as deduced. Such a process is referred to herein as “tagging” the information elements. The tagged information elements are then associated, in a database, with their respective deduced semantic roles and levels of certainty. The machine-learning techniques provided herein include supervised, unsupervised, and semi-supervised techniques. Embodiments described herein may be applied to data leakage prevention, cyber security, quality-of-service analysis, lawful interception, or any other relevant application.

A method and system for providing replacement text segments for a given text segment may include receiving a request to provide the replacement text segment for the text segment in the document, examining a content characteristic of the document, and examining at least one of user-specific information, organization-specific information, or non-linguistic features of the document, before identifying at least one replacement text segment for the text segment, via a machine translation system, based on the content characteristic of the document and at least one of the user-specific information, the organization-specific information, or the non-linguistic features of the document. The method and system may include providing the identified replacement text segment for display to a user, receiving an input indicating a user's selection of the identified replacement text segment, and upon receiving the input, replacing the text segment in the document with the identified replacement text segment.