Abstract: A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a context adaptive writing assistant configured to provide suggestions for improving the textual content, obtaining contextual information indicative of a level of formality of the textual content; and categorizing the textual content as being associated with a first level of formality selected from a plurality of levels of formality. The system is further implements analyzing the textual content to identify one or more suggested improvements to the textual content; selecting a subset of suggested improvements from the one or more suggested improvements to the textual content to the first user based on the first level of formality associated with the textual content; sending the subset of suggested improvements to the first client device; and causing the first client device to display the subset of suggested improvements.

Abstract: Systems and methods for generating artificial intelligence (AI) writing assistance include receiving a writing prompt to be used by an AI writing engine from a writing assistance client. The writing prompt is processed to provide indication of relevant user content items that can be referenced in the writing prompt. The content and metadata for any content items referenced in the writing prompts is retrieved and aggregated with the writing prompt to generate a request for writing assistance for the AI writing engine. Once writing feedback is received, the feedback is provided to the writing assistance wherein it can be displayed in the UI for acceptance by a user.

Abstract: A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a writing assistant configured to provide suggestions for improving the textual content. The textual content is analyzed by one or more NLP models configured to receive the textual content as an input and to provide candidate suggestions for correcting errors and/or otherwise improving the textual content. The candidate suggestions are associated with a suggestion category, and a determination is made by an autocorrection model whether autocorrection is appropriate for the suggestion category. The textual content is autocorrected based on those candidate suggestions for which autocorrection is appropriate while the remainder of the candidate suggestions may be presented to the user for approval prior to applying the candidate suggestions to the textual content.

Abstract: A computer implemented method includes receiving text corresponding to a span of text. Language detection is performed on the received text to generate multiple language candidates. The received text is also proofed over multiple languages to generate multiple language proofing critiques. A most likely language is generated for the received text as a function of the language candidates and the multiple language proofing critiques.

Abstract: A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a context adaptive writing assistant configured to provide suggestions for improving the textual content, obtaining contextual information indicative of a level of formality of the textual content; and categorizing the textual content as being associated with a first level of formality selected from a plurality of levels of formality. The system is further implements analyzing the textual content to identify one or more suggested improvements to the textual content; selecting a subset of suggested improvements from the one or more suggested improvements to the textual content to the first user based on the first level of formality associated with the textual content; sending the subset of suggested improvements to the first client device; and causing the first client device to display the subset of suggested improvements.

Abstract: Systems and methods for dynamically generating object models corresponding to regulations. According to certain aspects, a server computer may access a regulation and automatically generate a summary of the regulation based on a specific set of sentences. The server computer may additionally determine a set of topics and named-entity attributes for text within a regulation object model, as well as a probability that a topic or attribute is applicable to the regulation. Further, the server computer may generate and enrich object models according to the various analyses and avail the enriched object models for review by entities and users of regulatory compliance services.

Abstract: Systems and methods for dynamically generating object models corresponding to regulations. According to certain aspects, a server computer may access a regulation and automatically generate a summary of the regulation based on a specific set of sentences. The server computer may additionally determine a set of topics and named-entity attributes for text within a regulation object model, as well as a probability that a topic or attribute is applicable to the regulation. Further, the server computer may generate and enrich object models according to the various analyses and avail the enriched object models for review by entities and users of regulatory compliance services.

Abstract: Systems and methods for dynamically generating object models corresponding to regulations. According to certain aspects, a server computer may access a regulation and automatically generate a summary of the regulation based on a specific set of sentences. The server computer may additionally determine a set of topics and named-entity attributes for text within a regulation object model, as well as a probability that a topic or attribute is applicable to the regulation. Further, the server computer may generate and enrich object models according to the various analyses and avail the enriched object models for review by entities and users of regulatory compliance services.

Abstract: Enhanced spelling correction is provided. An enhanced spelling correction service may determine any misspellings (e.g., a word of the text containing an identified spelling error) in text using lexicon-based spelling correction. Each misspelling is assigned an error flag. The service can communicate each misspelling to a language model-based spell checker and receive, for each misspelling, an error confidence signal from the language model-based spell checker. For each misspelling having an error confidence signal indicating a low confidence that the identified spelling error is an actual spelling error, the service can determine whether to maintain or suppress the error flag by applying decision logic. In response to determining to maintain the error flag, the service can surface a visual indication of the spelling error. In response to determining to suppress the error flag, the service can suppress the error flag whereby the visual indication of the spelling error is not surfaced.

Abstract: A “Linguistic Complexity Tool” uses Machine Learning (ML) based techniques to predict “source complexity scores” for localization of source language assets or resources (i.e., “source content”), or subsections of that content, to provide users with predicted levels of difficulty in localizing source content into target languages, dialects, or linguistic styles. These predicted source complexity scores provide a number of advantages, including but not limited to, improved user efficiency and user interaction performance by identifying source content, or subsections of that content, that are likely to be difficult or time consuming for users to localize. Further, these source complexity scores enable users to modify source content prior to localization to provide lower source complexity scores, thereby reducing error rates with respect to localized text or language presented in software applications or other media including, but not limited to, spoken or written localizations of the source content.