Patent ID: 11907858
Assignee: YAHOO ASSETS LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:
retrieving, by a server, a document from a source via a network connection;
analyzing the document to identify a plurality of mentions, comprising a first mention and a second mention, in the document;
processing a plurality of documents extracted from a knowledge base to generate processed documents by transforming one or more hyperlinks in each document to a canonical form for an associated entity;
training an entity embedding model using the processed documents, wherein a first layer of the entity embedding model models context of entities of the knowledge base and a second layer of the entity embedding model models context of text spans of the knowledge base;
generating, using the entity embedding model, a graph with a plurality of nodes comprising a first node corresponding to the first mention and a second node corresponding to the second mention;
assigning a weight to an edge connecting the first node to the second node;
determining that the first node is assigned a first label corresponding to a first candidate entity;
randomly traversing the graph, using a traversal logic, to determine whether to label a node and, upon determining not to label the node, determine whether to propagate a label of a neighboring node to the node;
propagating the first label assigned to the first node to the second node based upon the weight of the edge;
determining a topic of the document based upon (i) the propagating of the first label assigned to the first node to the second node and (ii) local context corresponding to one or more verbs used in the document being indicative of actions associated with a first profession associated with the first candidate entity;
retrieving, by the server, a second document from the source via a second network connection;
analyzing the second document to identify a second plurality of mentions, comprising a third mention and a fourth mention, in the second document;
generating, using the entity embedding model, a second graph with a second plurality of nodes comprising a third node corresponding to the third mention and a fourth node corresponding to the fourth mention;
assigning a second weight to a second edge connecting the third node to the fourth node;
determining that the third node is assigned a third label corresponding to a second candidate entity;
propagating the third label assigned to the third node to the fourth node based upon the second weight of the second edge;
determining a second topic of the second document based upon (i) the propagating of the third label assigned to the third node to the fourth node and (ii) second local context corresponding to one or more second verbs used in the document being indicative of actions associated with a second profession associated with the second candidate entity; and
organizing and presenting the document and the second document among a plurality of articles based upon the topic and the second topic.