| CPC G06F 40/205 (2020.01) [G06F 40/30 (2020.01)] | 18 Claims |
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1. A computer-implemented method comprising:
receiving a set of graph predictions corresponding to an input text, where each graph prediction of the set of graph predictions is generated by a different machine learning model and each graph prediction is a semantic representation graph of the input text;
for each graphs prediction:
comparing a current graph prediction to each other graph prediction of the set of graph predictions;
counting, for each other graph prediction, a vote for each common edge and node; and
blending, based on the comparing and the counting, the current graph prediction to generate a first blended graph; and
adding the first blended graphs to a plurality of candidate blended graphs, where nodes and edges of the candidate blended graphs have respective selection metric values, generated using a selection metric function, that meet a minimum threshold; and
selecting as an output blended graph a candidate blended graph of the plurality of candidate blended graphs having a highest total combination of selection metric values among the plurality of candidate blended graphs.
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