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

Claim 0:
1. A computer-implemented method for linking a mention to an entity in one or more documents hosted on at least one of a document host or a document portal, comprising:
receiving a request to link mentions in a document to entities, wherein the document is an article published by a source;
retrieving, by a server, the document from the source via a network connection;
processing the retrieved document by:
scanning the document, using at least one of natural language processing or word/phrase matching, to identify a plurality of mentions, in the document, comprising a first mention in the document and a second mention in the document;
searching a knowledge base for candidate entities that match the first mention identified in the document and retrieving, from the knowledge base, a first set of candidate entities, comprising a first candidate entity and a second candidate entity, corresponding to the first mention; and
searching the knowledge base for candidate entities that match the second mention identified in the document and retrieving, from the knowledge base, a second set of candidate entities, comprising a third candidate entity and a fourth candidate entity, corresponding to the second mention;

processing a plurality of documents extracted from the 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 is associated with modeling context of entities of the knowledge base and a second layer of the entity embedding model is associated with modeling context of text spans of the knowledge base;
generating, using the entity embedding model and based upon a forward pass of the plurality of mentions, a first matrix indicative of a first likelihood of the first mention corresponding to the first candidate entity, a second likelihood of the first mention corresponding to the second candidate entity, a third likelihood of the second mention corresponding to the third candidate entity, and a fourth likelihood of the second mention corresponding to the fourth candidate entity;
generating, using the entity embedding model and based upon a backward pass of the plurality of mentions, a second matrix indicative of a fifth likelihood of the first mention corresponding to the first candidate entity, a sixth likelihood of the first mention corresponding to the second candidate entity, a seventh likelihood of the second mention corresponding to the third candidate entity, and an eighth likelihood of the second mention corresponding to the fourth candidate entity;
selecting, from the first set of candidate entities, the first candidate entity but not the second candidate entity and linking the first candidate entity but not the second candidate entity to the first mention based upon the first matrix and the second matrix;
selecting, from the second set of candidate entities, the third candidate entity but not the fourth candidate entity and linking the third candidate entity but not the fourth candidate entity to the second mention based upon the first matrix and the second matrix, wherein at least one of the selecting the first candidate entity or the selecting the third candidate entity is based upon at least one of:
local context corresponding to one or more verbs used in the article being indicative of actions associated with a first profession associated with at least one of the first candidate entity or the third candidate entity, wherein one or more second verbs not used in the article are indicative of actions associated with a second profession associated with at least one of the second candidate entity or the fourth candidate entity; or
locational context determined based upon GPS of a device of a user;

determining a topic of the article based upon the linking of the first candidate entity but not the second candidate entity to the first mention and the linking of the third candidate entity but not the fourth candidate entity to the second mention;
receiving a second request to link second mentions in a second document to second entities, wherein the second document is a second article published by a second source;
retrieving, by the server, the second document from the source via a second network connection;
processing the retrieved second document by:
scanning the second document, using at least one of natural language processing or word/phrase matching, to identify a second plurality of mentions, in the second document, comprising a third mention and a fourth mention;
searching the knowledge base for candidate entities that match the third mention identified in the second document and retrieving, from the knowledge base, a third set of candidate entities, comprising a fifth candidate entity and a sixth candidate entity, corresponding to the third mention; and
searching the knowledge base for candidate entities that match the fourth mention identified in the second document and retrieving, from the knowledge base, a fourth set of candidate entities, comprising a seventh candidate entity and an eighth candidate entity, corresponding to the fourth mention;

generating, using the entity embedding model and based upon a forward pass of the second plurality of mentions, a third matrix indicative of a ninth likelihood of the third mention corresponding to the fifth candidate entity, a tenth likelihood of the third mention corresponding to the sixth candidate entity, an eleventh likelihood of the fourth mention corresponding to the seventh candidate entity, and a twelfth likelihood of the fourth mention corresponding to the eighth candidate entity;
generating, using the entity embedding model and based upon a backward pass of the second plurality of mentions, a fourth matrix indicative of a thirteenth likelihood of the third mention corresponding to the fifth candidate entity, a fourteenth likelihood of the third mention corresponding to the sixth candidate entity, a fifteenth likelihood of the fourth mention corresponding to the seventh candidate entity, and a sixteenth likelihood of the fourth mention corresponding to the eighth candidate entity;
selecting, from the third set of candidate entities, the fifth candidate entity but not the sixth candidate entity and linking the fifth candidate entity but not the sixth candidate entity to the third mention based upon the third matrix and the fourth matrix;
selecting, from the fourth set of candidate entities, the seventh candidate entity but not the eighth candidate entity and linking the seventh candidate entity but not the eighth candidate entity to the fourth mention based upon the third matrix and the fourth matrix, wherein at least one of the selecting the fifth candidate entity or the selecting the seventh candidate entity is based upon at least one of:
second local context corresponding to one or more third verbs used in the second article being indicative of actions associated with a third profession associated with at least one of the fifth candidate entity or the seventh candidate entity, wherein one or more fourth verbs not used in the second article are indicative of actions associated with a fourth profession associated with at least one of the sixth candidate entity or the eighth candidate entity; or
second locational context determined based upon GPS of a second device of a second user;

determining a second topic of the second article based upon the linking of the fifth candidate entity but not the sixth candidate entity to the third mention and the linking of the seventh candidate entity but not the eighth candidate entity to the fourth mention; and
organizing and presenting, by the server, the article and the second article among a plurality of articles on at least one of a document host or a document portal based upon the topic of the article and the second topic of the second article.