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US Patent 12086552 Generating semantic vector representation of natural language data

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Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
120865520
Patent Inventor Names
Gabriele Picco0
Vanessa Lopez Garcia0
Thanh Lam Hoang0
Date of Patent
September 10, 2024
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Patent Application Number
176564180
Date Filed
March 24, 2022
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Patent Citations
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US Patent 10474962 Semantic entity relation detection classifier training
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US Patent 11354479 Post-CTS clock tree restructuring with ripple move
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US Patent 11620428 Post-CTS clock tree restructuring
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US Patent 11669690 Method and apparatus for processing sematic description of text entity, and storage medium
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US Patent 11636270 Methods and systems for generating a semantic computation graph for understanding and grounding referring expressions
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US Patent 11704486 Abstract meaning representation parsing with graph translation
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US Patent 7302382 Generating with lexical functional grammars
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US Patent 7484199 Buffer insertion to reduce wirelength in VLSI circuits
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Patent Primary Examiner
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Thuykhanh Le
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CPC Code
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G06N 5/022
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G06N 3/04
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G06F 40/30
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G06F 40/205
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G06N 3/08
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Patent abstract

A computer-implemented method for automatically generating a semantic vector representation of a relation between a specific set of entities in natural language text is provided. The method may include, in response to receiving a text segment comprising a set of entities, automatically parsing the text segment into an abstract meaning representation (AMR) graph comprising nodes representing the set of entities. The method may further include extracting a number of minimum Steiner trees from the AMR graph, and wherein each Steiner tree comprises a minimum amount of edges between the nodes corresponding to a first entity and at least one second entity. The method may further include using a trained graph neural network (GNN) to determine vector embeddings for the minimum Steiner trees. The method may further include aggregating the vector embeddings to generate the semantic vector representation of the relation between the specific set of entities.

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