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: The present disclosure relates to a method and device for generating a finite state automata for recognizing a chemical name in a text, and a recognition method. According to an embodiment of the present disclosure, the method comprises substituting representation constants of categories of character segments appearing in an organic compound name set into the organic compound name set to obtain a conversion name set; updating the conversion name set based on a conversion name segment which repeatedly appears in the conversion name set; and generating the finite state automata based on the updated conversion name set.

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.

Abstract: The present disclosure relates to a method and device for generating a finite state automata for recognizing a chemical name in a text, and a recognition method. According to an embodiment of the present disclosure, the method comprises substituting representation constants of categories of character segments appearing in an organic compound name set into the organic compound name set to obtain a conversion name set; updating the conversion name set based on a conversion name segment which repeatedly appears in the conversion name set; and generating the finite state automata based on the updated conversion name set.