In an example, an article may be analyzed to identify entity terms. Entity term relevance scores associated with the entity terms may be determined based upon the article and the entity terms. One or more first entity terms may be selected based upon the entity term relevance scores. One or more sets of reference position information associated with the one or more first entity terms may be determined. A first set of reference position information is based upon one or more positions, in the article, of one or more references to a first entity term. One or more second entity terms of the one or more first entity terms may be selected based upon the one or more sets of reference position information. A set of one or more salient entity tags associated with the article may be generated based upon the one or more second entity terms.

A mechanism is provided in a data processing system to implement a medical concept searching engine for improving searches of medical concepts based on an index model. The mechanism generates a concept index model data structure that records medical concepts and corresponding numbers of instances of the medical concepts in the corpus of documents. Responsive to receiving a search request from a user, the medical concept searching engine identifies at least one medical concept in the search request and one or more related medical concepts that are related to the at least one medical concept based on an ontology data structure. The medical concept searching engine generates a bubble graph user interface comprising a plurality of bubbles corresponding to the at least one medical concept and the one or more related medical concepts.

Methods and systems are presented for analyzing feedback data associated with a content and generating an interactive graphical representation of the feedback data. Upon receiving a request from a user, a feedback analysis system may access feedback data associated with a content from a content hosting server. The feedback data may include comments submitted by viewers of the content. The feedback analysis system may analyze the comments and generate an interactive graphical representation of the feedback data. The interactive graphical representation may include icons that represents keywords that are relevant to the comments and sentiments of the viewers derived based on the comments. Upon receiving a selection of an icon, the feedback analysis system may present a comment that corresponds to the keyword and/or sentiment represented by the icon.

State of art techniques that utilize spatial association based Table structure Recognition (TSR) have limitation in selecting minimal but most informative word pairs to generate digital table representation. Embodiments herein provide a method and system for TSR from an table image via deep spatial association of words using optimal number of word pairs, analyzed by a single classifier to determine word association. The optimal number of word pairs are identified by utilizing immediate left neighbors and immediate top neighbors approach followed redundant word pair elimination, thus enabling accurate capture of structural feature of even complex table images via minimal word pairs. The reduced number of word pairs in combination with the single classifier trained to determine the word associations into classes comprising as same cell, same row, same column and unrelated, provides TSR pipeline with reduced computational complexity, consuming less resources still generating more accurate digital representation of complex tables.

Automatic decisioning associated with unstructured data is disclosed. Unstructured data, such as that associated with comments of an underwriter regarding a credit decision, can be received. Text mining can be performed to extract features from the unstructured data. The extracted features can subsequently be provided as input to a machine learning model configured to return a prediction of a class associated with the unstructured data. The predicted class, such as approved or rejected, can subsequently be conveyed for display on a display device.

A system, server device, and method are provided for managing document metadata. The method includes storing metadata in a graph separately from documents with which the metadata is associated and connecting metadata in the graph according to a hierarchy, with each node of the graph being classified based on metadata content. The method also includes assigning properties to edges in the graph connecting the nodes; receiving, via a communications module, an instruction to update the metadata; and dynamically updating at least one edge or node in the graph according to the instruction without requiring access to an associated document. The method also includes receiving, via the communications module, an inquiry related to a document; and responding to the inquiry, via the communications module, after accessing metadata associated with the document by navigating the graph.

A method includes receiving a user query and performing, using at least one processor, multiple rounds of an answer generation process. Each round of the answer generation process includes selecting one of multiple functions to be performed based on an input state. The input state for each round includes an embedding of the user query in a feature space. The input state for at least one round also includes an embedding of information to be used to identify an answer to the user query in the feature space. Each round of the answer generation process also includes performing the selected function. The multiple functions include (i) an answer generation function that produces the answer to the user query and (ii) at least one additional function that updates the input state for a current round for use during a subsequent round. In addition, the method includes providing the answer to the user.

Humans have collaborated in both small and large groups for thousands of years, sometimes achieving great feats for the good of all mankind. However, most groups have failed to achieve greatness, often because of poor group “chemistry” and/or because of missing skills or insights. These problems typically arise because group organizers build groups using human mental processes that suffer from social, racial, gender, ethnic, or other unconscious biases. To reduce impact of these biases and help organizers form more effective groups or teams, the present inventors have devised, among other things, an exemplary system that subdivides a group of user data structures into subgroups, based on similarities between electronic surveys and/or other data types, such as social media and network analysis data types. The system further includes user interfaces for creating surveys, implementing user preferences regarding subgroup membership and sizes, controlling the subdivision process, and displaying the subgroups.

Systems and methods for natural language processing are described. One or more embodiments of the present disclosure identify a claim from a document, wherein the claim corresponds to a topic, create a graph comprising a plurality of nodes having a plurality of node types and a plurality of edges having a plurality of edge types, wherein one of the nodes represents the claim, and wherein each of the edges represents a relationship between a corresponding pair of the nodes, encode the claim based on the graph using a graph convolutional network (GCN) to obtain an encoded claim, classify the claim by decoding the encoded claim to obtain a stance label that indicates a stance of the claim towards the topic, and transmit information indicating a viewpoint of the document towards the topic based on the stance label.

A core data-based storage method for radial multidimensional documents according to an embodiment includes classifying first documents with same document type information into a preset folder to obtain a classified folder; acquiring the total number of levels of document level information of all non-core documents; and displaying preset target images at core display positions and/or non-core display positions, and setting a corresponding storage link path for each of the displayed preset target images. The documents may be stored by classification and displayed visually.