| CPC G06N 5/022 (2013.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01)] | 20 Claims |

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1. A computer-implemented method for automatically locating a taxonomy candidate into an existing taxonomic hierarchy, the computer-implemented method comprising:
training a neural taxonomy expander according to the existing taxonomic hierarchy, wherein training the neural taxonomy expander includes:
generating a plurality of candidate/hypernym pairs, the plurality of candidate/hypernym pairs including positive candidate/hypernym pairs and negative candidate/hypernym pairs;
subdividing the plurality of candidate/hypernym pairs into a training set and a validation set;
repeatedly, until a predetermined loss threshold from processing the validation set is met:
training the neural taxonomy expander according to the training set until the predetermined loss threshold is met in processing the training set;
validating the neural taxonomy expander according to the validation set; and
updating the neural taxonomy expander according to results of the training upon a determination that the predetermined loss threshold for the validation set is not met; and
generating an executable neural taxonomy expander according to the trained neural taxonomy expander;
generating an embedding vector of the taxonomy candidate;
projecting the embedding vector of the taxonomy candidate into a taxonomic hyperspace according to a taxonomy projection by the executable neural taxonomy expander;
identifying a set of closest neighbors of the existing taxonomic hierarchy to the projected embedding vector of the taxonomy candidate;
determining a closest neighbor of the identified set of closest neighbors as an immediate parent of the taxonomy candidate; and
adding the taxonomy candidate into the existing taxonomic hierarchy as a child node of the determined immediate parent.
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