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US Patent 11526688 Discovering ranked domain relevant terms using knowledge

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Is a
Patent
Patent
1

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
115266881
Patent Inventor Names
Gaetano Rossiello1
Yu Deng1
Sarthak Dash1
Ruchi Mahindru1
Nicolas Rodolfo Fauceglia1
Nandana Mihindukulasooriya1
Md Faisal Mahbub Chowdhury1
Alfio Massimiliano Gliozzo1
Date of Patent
December 13, 2022
1
Patent Application Number
168507351
Date Filed
April 16, 2020
1
Patent Citations
‌
US Patent 10229164 Adjusting a relevancy score of a keyword cluster—time period—event category combination based on event related information
1
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US Patent 10262079 Determining anonymized temporal activity signatures of individuals
1
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US Patent 10496691 Clustering search results
1
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US Patent 10621183 Method and system of an opinion search engine with an application programming interface for providing an opinion web portal
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US Patent 11288453 Key-word identification
1
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US Patent 11201890 System and method for adaptive graphical depiction and selective remediation of cybersecurity threats
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US Patent 11294974 Golden embeddings
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US Patent 10157226 Predicting links in knowledge graphs using ontological knowledge
1
...
Patent Primary Examiner
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Anne L Thomas-Homescu
1
CPC Code
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G06F 40/40
1
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G06F 40/30
1
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G06N 20/00
1
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G06K 9/6232
1
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G06K 9/6218
1
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G06K 9/6215
1
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G06K 9/623
1
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G06F 40/205
1
...

One embodiment of the invention provides a method for terminology ranking for use in natural language processing. The method comprises receiving a list of terms extracted from a corpus, where the list comprises a ranking of the terms based on frequencies of the terms across the corpus. The method further comprises accessing a domain ontology associated with the corpus, and re-ranking the list based on the domain ontology. The resulting re-ranked list comprises a different ranking of the terms based on relevance of the terms using knowledge from the domain ontology. The method further comprises generating clusters of terms via a trained model adapted to the corpus, and boosting a rank of at least one term of the re-ranked list based on the clusters to increase a relevance of the at least one term using knowledge from the trained model.

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