Abstract: A method of presenting prompt information by utilizing a neural network which includes a BERT model and a graph convolutional neural network (GCN), comprising: generating a first vector based on a combination of an entity, a context of the entity, a type of the entity and a part of speech of the context by using BERT model; generating a second vector based on each of predefined concepts by using BERT model; generating a third vector based on a graph which is generated based on the concepts and relationships thereamong, by using GCN; generating a fourth vector by concatenating the second and third vectors; calculating semantic similarity between the entity and each concept based on the first and fourth vectors; determining, based on the first vector and the semantic similarity, that the entity corresponds to one of the concepts; and generating the prompt information based on the determined concept.

Abstract: The present disclosure relates to a method and device of generating an extended pre-trained language model and a natural language processing method. The method of generating an extended pre-trained language model comprises training the extended pre-trained language model in an iterative manner. Training the extended pre-trained language model comprises: generating, based on a mask for randomly hiding a word in a sample sentence containing an unregistered word, an encoding feature of the sample sentence; generating a predicted hidden word based on the encoding feature; and adjusting the extended pre-trained language model based on the predicted hidden word.

Abstract: A method of presenting prompt information includes: generating a mask vector for an entity, which is used to identify a position of the entity in a statement composed of the entity and context; generating a first vector and a second vector based on the entity and the context; generating a third vector based on the mask vector and the second vector; concatenating the first vector and the third vector to generate a fourth vector; predicting which concept of multiple predefined concepts the entity corresponds to, based on the fourth vector by a first classifier; predicting which type of multiple predefined types the entity corresponds to, based on the fourth vector by a second classifier; jointly training the first the second classifiers; determining a concept to which the entity corresponds based on prediction result of the trained first classifier; and generating the prompt information based on the determined concept.

Abstract: A method of presenting prompt information by utilizing a neural network which includes a BERT model and a graph convolutional neural network (GCN), comprising: generating a first vector based on a combination of an entity, a context of the entity, a type of the entity and a part of speech of the context by using BERT model; generating a second vector based on each of predefined concepts by using BERT model; generating a third vector based on a graph which is generated based on the concepts and relationships thereamong, by using GCN; generating a fourth vector by concatenating the second and third vectors; calculating semantic similarity between the entity and each concept based on the first and fourth vectors; determining, based on the first vector and the semantic similarity, that the entity corresponds to one of the concepts; and generating the prompt information based on the determined concept.