| CPC G06F 18/214 (2023.01) [G06F 18/22 (2023.01); G06F 40/295 (2020.01); G06F 40/30 (2020.01)] | 20 Claims |
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1. A processor-implemented method of training an embedding vector generation model, the method comprising:
identifying a keyword in a query sentence;
generating an embedding vector of the query sentence and an embedding vector of the keyword based on the embedding vector generation model; and
training the embedding vector generation model such that a first similarity between the embedding vector of the query sentence and the embedding vector of the keyword is greater than a second similarity between an embedding vector of a reference sentence and the embedding vector of the keyword,
wherein the embedding vector generation model is pre-trained based on any one or any combination of a general sentence and a conversational sentence, and
wherein the keyword comprises at least one word in the query sentence and a length of the keyword is less than a length of the general sentence or the conversational sentence.
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