METHODS AND SYSTEMS FOR USING MACHINE-LEARNING EXTRACTS AND SEMANTIC GRAPHS TO CREATE STRUCTURED DATA TO DRIVE SEARCH, RECOMMENDATION, AND DISCOVERY
Methods and systems for using a combination of semantic graphs and machine learning to automatically generate structured data, recognize important entities/keywords, and create weighted connections for more relevant search results and recommendations. For example, by inferring relevant entities, metadata results are richer and more meaningful, enabling faster decision-making for the consumer and stronger viewership for the content owner.
1 . A method of providing content recommendations by automatically determining relevancies of entities in text strings, the method comprising:
receiving, by a user input interface, a text string;
identifying, by control circuitry, a pronoun in the text string;
resolving, by the control circuitry, the pronoun into a noun to create a resolved text string;
identifying, by the control circuitry, a noun chunk in the resolved text string;
processing, by the control circuitry, the noun chunk using a classifier based on a semantic graph featuring a plurality of nodes, wherein each of the plurality of nodes is scored based on a closeness centrality metric and a betweenness centrality metric, wherein the closeness centrality metric is a measure of a sum of a length of a shortest path between a respective node and each of the other nodes in the semantic graph, and wherein the betweenness centrality metric is a measure of centrality in the semantic graph of a respective node;
determining, by the control circuitry, an entity based on processing the noun chunk using the classifier; and
generating for display, on a display device, the entity in response to the received text string.
2 . The method of claim 1 , wherein the semantic graph comprises a plurality of nodes, wherein each of the plurality of nodes corresponds to an entity from a dataset of entities.
3 . The method of claim 1 , wherein determining an entity based on processing the noun chunk using the classifier, comprises:
scoring each entity;
ranking each entity based on its respective score; and
selecting the entity with the highest score.
4 . The method of claim 3 , wherein each entity is scored on seven text features and two graph features.
5 . The method of claim 1 , wherein the classifier is a Decision Tree Classifier or a Random Forest Classifier.
6 . The method of claim 1 , wherein generating for display the entity in response to the received text string comprises generating for display the entity in a search, recommendation, or discovery feature.
7 . The method of claim 1 , wherein the text string is received from a user or from an electronic device.
8 . The method of claim 1 , wherein resolving the pronoun into the noun to create the resolved text string, comprises resolving the pronoun using coreference resolution.
9 . The method of claim 1 , wherein identifying the noun chunk in the resolved text string comprises identifying the noun chunk using part-of-speech tagging.
10 . The method of claim 1 , wherein the semantic graph is a knowledge base that represents semantic relations between concepts in a network.
11 . A system of providing content recommendation by automatically determining relevancies of entities in text strings, the system comprising:
memory; and
control circuitry configured to:
receive a text string;
identify a pronoun in the text string;
resolve the pronoun into a noun to create a resolved text string;
identify a noun chunk in the resolved text string;
process the noun chunk using a classifier based on a semantic graph featuring a plurality of nodes, wherein each of the plurality of nodes is scored based on a closeness centrality metric and a betweenness centrality metric, wherein the closeness centrality metric is a measure of a sum of a length of a shortest path between a respective node and each of the other nodes in the semantic graph, and wherein the betweenness centrality metric is a measure of centrality in the semantic graph of a respective node;
determine an entity based on processing the noun chunk using the classifier; and
generate for display the entity in response to the received text string.
12 . The system of claim 11 , wherein the semantic graph comprises a plurality of nodes, wherein each of the plurality of nodes corresponds to an entity from a dataset of entities.
13 . The system of claim 11 , wherein determining an entity based on processing the noun chunk using the classifier, comprises:
scoring each entity;
ranking each entity based on its respective score; and
selecting the entity with the highest score.
14 . The system of claim 13 , wherein each entity is scored on seven text features and two graph features.
15 . The system of claim 11 , wherein the classifier is a Decision Tree Classifier or a Random Forest Classifier.
16 . The system of claim 11 , wherein generating for display the entity in response to the received text string comprises generating for display the entity in a search, recommendation, or discovery feature.
17 . The system of claim 11 , wherein the text string is received from a user or from an electronic device.
18 . The system of claim 11 , wherein resolving the pronoun into the noun to create the resolved text string, comprises resolving the pronoun using coreference resolution.
19 . The system of claim 11 , wherein identifying the noun chunk in the resolved text string comprises identifying the noun chunk using part-of-speech tagging.
20 . The system of claim 11 , wherein the semantic graph is a knowledge base that represents semantic relations between concepts in a network.
21 - 50 . (canceled)