Abstract: A system and method for providing screen-aware adjustment visualizations associated with displays are provided. The system may facilitate training a large language model to generate a plurality of types of content to improve text content associated with at least one document. The system may further facilitate extracting and reconstructing items of content from a portable document format document. The system may further maintain a semantic order of elements associated with the content. The system may further dynamically organize the content within a display of a device.

Abstract: Technologies pertaining to electronic document understanding are described herein. A document is received, wherein the document includes a section of a type. An image of the document is generated, and a candidate region is identified in the image of the document, wherein the candidate region encompasses the section. A label is assigned to the candidate region based upon text of the section, wherein the label identifies the type of the section. An electronic document understanding task is performed based upon the label assigned to the candidate region.

Abstract: A technique produces a graph data structure based on at least partially unstructured information dispersed over web documents. The technique involves applying a machine-trained model to a set of documents (or, more generally “document units”) to identify topics in the documents. The technique then generates count information by counting the occurrences of the single topics and co-occurrences of parings of topics in the documents. The technique generates conditional probability information based on the count information. An instance of conditional probability information describes a probability that a first topic will occur, given an appearance of a second topic, and a probability that the second topic will occur, given an appearance of the first topic. The technique then formulates the conditional probability information in a graph data structure. The technique also provides an application system that utilizes the graph data structure to provide any kind of computer-implemented service to a user.

Abstract: In some implementations, the disclosed systems and methods can automatically generate seller listing titles and descriptions for products; set a follow-me mode for various virtual objects, causing the virtual objects to be displayed as word-locked or body-locked in response to a current mode for the virtual objects and the location of the user of the XR device in relation to various anchor points for the virtual objects; create and/or apply XR profiles that specify one or more triggers for one or more effects that are applied to a user when the triggers are satisfied; and/or enable addition of external content in 3D applications.

Abstract: Technologies pertaining to ranking webpages in response to receipt of a query are described. A search engine receives a query and identifies webpages that are germane to the query. The search engine ranks the identified webpages to form a ranked list, wherein a first webpage is positioned in the ranked list based upon a static score assigned to the first webpage. The static score is based upon a weight assigned to a hyperlink in a second webpage, wherein the hyperlink points to the first webpage, and further wherein the weight is based upon a value of a feature of the hyperlink, such as a location of the hyperlink on the second webpage when the second webpage is rendered. Further, the second webpage includes several hyperlinks that point to different webpages, wherein each of the several hyperlinks has a different weight assigned thereto.

Abstract: A technique produces a graph data structure based on at least partially unstructured information dispersed over web documents. The technique involves applying a machine-trained model to a set of documents (or, more generally “document units”) to identify topics in the documents. The technique then generates count information by counting the occurrences of the single topics and co-occurrences of parings of topics in the documents. The technique generates conditional probability information based on the count information. An instance of conditional probability information describes a probability that a first topic will occur, given an appearance of a second topic, and a probability that the second topic will occur, given an appearance of the first topic. The technique then formulates the conditional probability information in a graph data structure. The technique also provides an application system that utilizes the graph data structure to provide any kind of computer-implemented service to a user.

Abstract: Technologies pertaining to electronic document understanding are described herein. A document is received, wherein the document includes a section of a type. An image of the document is generated, and a candidate region is identified in the image of the document, wherein the candidate region encompasses the section. A label is assigned to the candidate region based upon text of the section, wherein the label identifies the type of the section. An electronic document understanding task is performed based upon the label assigned to the candidate region.

Abstract: Technologies pertaining to ranking webpages in response to receipt of a query are described. A search engine receives a query and identifies webpages that are germane to the query. The search engine ranks the identified webpages to form a ranked list, wherein a first webpage is positioned in the ranked list based upon a static score assigned to the first webpage. The static score is based upon a weight assigned to a hyperlink in a second webpage, wherein the hyperlink points to the first webpage, and further wherein the weight is based upon a value of a feature of the hyperlink, such as a location of the hyperlink on the second webpage when the second webpage is rendered. Further, the second webpage includes several hyperlinks that point to different webpages, wherein each of the several hyperlinks has a different weight assigned thereto.

Abstract: Context-based multi-granularity intent discovery, in relation to determining the intent of a user-problem associated with one of a plurality of products, includes identifying each of the plurality of products in a base intent level of a hierarchical intent data structure, creating a first granularity level below the base intent level, and creating a subsequent granularity level. The first granularity level includes groupings of the plurality of user-problems created according to a first common intent feature; the common intent feature is related to a respective one of the plurality of products. The subsequent granularity level includes sub-groupings of the user-problems of a respective grouping with each sub-grouping created according to a second common intent feature. The second common intent feature of each sub-grouping is determined without consideration of the first common intent feature.

Abstract: A technique is described herein for processing network-accessible documents in a scalable and resource-efficient manner. A model-generating process provided by the technique includes three-phases. A first phase generates a set of sample documents associated with a particular class of documents, a second phase applies labels to the sample documents to produce a set of labeled documents, and a third phase generates at least one data-extraction model based on the set of labeled documents. The data-extraction model includes data-extracting logic for extracting at least one specified data item from new documents that match the class of documents. In a data-extracting process, the technique identifies a data-extraction model that applies to the new document and then applies that model.

Abstract: Configurations herein comprise a multi-layer framework to extract document structural data. The framework extracts structural data from raw, unstructured, electronic documents, for example, .pdf documents. Structural data refers to the semantic elements, for example, paragraphs, lists, tables, titles etc. that may be visible in the displayed document but not described in electronic data.

Abstract: A technique is described herein that receives a set of markup-language documents (e.g., HTML documents). The technique produces a set of object-model representations (VDOMs) based on the set of markup-language documents. For a given markup-language document, the producing operation involves accessing and processing information that is linked to the markup-language document. Each object-model representation also includes visual layout information that specifies a visual appearance of a page that would be produced upon display of the markup-language document by a browser application. The technique then generates transformation logic based on the set of markup-language documents and their corresponding VDOMs. The transformation logic, when applied to a new markup-language document, converts the new markup-language document into a corresponding VDOM without retrieving at least some information identified by links (if any) in the new markup-language document.

Abstract: Aspects herein comprise a browser application (for example, an extension) that allows an end user to provide annotated web content to an extraction service. A client-side user can execute the application to select and annotate the data on a web page, and the annotation can indicate a location of and an identification of the kind of data that is in the web page. Then, based on the annotated web pages, one or more template(s)/rule(s) can be developed for the web page. The templates/rules can then be analyzed to extract automatically the structure data for the web page, which can be provided to the user. The template(s)/rule(s) can be uploaded to an extraction service, which collects and manages the template(s)/rule(s). Then, the extraction service can send extracted structure data to end users or other applications.

Abstract: Context-based multi-granularity intent discovery, in relation to determining the intent of a user-problem associated with one of a plurality of products, includes identifying each of the plurality of products in a base intent level of a hierarchical intent data structure, creating a first granularity level below the base intent level, and creating a subsequent granularity level. The first granularity level includes groupings of the plurality of user-problems created according to a first common intent feature; the common intent feature is related to a respective one of the plurality of products. The subsequent granularity level includes sub-groupings of the user-problems of a respective grouping with each sub-grouping created according to a second common intent feature. The second common intent feature of each sub-grouping is determined without consideration of the first common intent feature.

Abstract: A system and method for automatically detecting and extracting entity data from a web page is provided. The method may include detecting a pattern for an entity based on a visual layout of the web page. A region of the webpage corresponding to the pattern may be identified as including the entity data, where the entity data is in a semi-structured form. Within the region, properties associated with the entity may be detected, annotations for the properties may be determined, and a category for the entity may be identified, where the properties, annotations, and category may be used to construct a schema for a structured form of the entity data. A template may be generated based on the schema and applied to the web page to extract the entity data in the structured form.