Abstract: The present disclosure discloses a method, apparatus, device, and storage medium for training a model, relates to the technical fields of knowledge graph, natural language processing, and deep learning. The method may include: acquiring a first annotation data set, the first annotation data set including sample data and a annotation classification result corresponding to the sample data; training a preset initial classification model based on the first annotation data set to obtain an intermediate model; performing prediction on the sample data in the first annotation data set using the intermediate model to obtain a prediction classification result corresponding to the sample data; generating a second annotation data set based on the sample data, the corresponding annotation classification result, and the corresponding prediction classification result; and training the intermediate model based on the second annotation data set to obtain a classification model.

Abstract: A search method based on a neural network model is provided. The neural network model includes a semantic representation model, a recall model, and a ranking model. The present disclosure relates to the field of artificial intelligence, and in particular to the technical field of search. An implementation of the method comprises: inputting a target search and a plurality of objects to be matched to the semantic representation model to obtain a first output of the semantic representation model; inputting the first output of the semantic representation model to the recall model, and obtaining at least one recall object matching the target search from the plurality of objects to be matched by using the recall model; and inputting a second output of the semantic representation model to the ranking model, and obtaining a matching value of each of the at least one recall object by using the ranking model.

Abstract: The present disclosure provides a multi-system-based intelligent question answering method and apparatus, and a device, relating to the field of artificial intelligence, in particular to the field of knowledge graph. The specific implementation solution is: determining a question category of question information in response to a question answering instruction of a user, wherein the question answering instruction is used to indicate the question information; determining a query engine corresponding to the question category, and invoking multiple question analysis systems corresponding to the query engine according to the query engine; and feeding back answer information to the user when the answer information corresponding to the question information is determined according to a current question analysis system in a process of processing the question information by sequentially using the multiple question analysis systems according to system priorities of the question analysis systems.

Abstract: The present disclosure discloses a method, apparatus, device, and storage medium for training a model, relates to the technical fields of knowledge graph, natural language processing, and deep learning. The method may include: acquiring a first annotation data set, the first annotation data set including sample data and a annotation classification result corresponding to the sample data; training a preset initial classification model based on the first annotation data set to obtain an intermediate model; performing prediction on the sample data in the first annotation data set using the intermediate model to obtain a prediction classification result corresponding to the sample data; generating a second annotation data set based on the sample data, the corresponding annotation classification result, and the corresponding prediction classification result; and training the intermediate model based on the second annotation data set to obtain a classification model.

Abstract: The present disclosure discloses a method, apparatus, device, and storage medium for matching semantics, relates to the technical fields of knowledge graph, natural language processing, and deep learning. The method may include: acquiring a first text and a second text; acquiring language knowledge related to the first text and the second text; determining a target embedding vector based on the first text, the second text, and the language knowledge; and determining a semantic matching result of the first text and the second text, based on the target embedding vector.

Abstract: The disclosure discloses a semantics processing method, a semantics processing apparatus, an electronic device, and a medium, and relates to a field of knowledge graph technologies. The detailed implementation includes: determining a target semantic element rule matching a text to be parsed, and parsing the text to be parsed by employing the target semantic element rule to obtain a semantic element parsing result; generating a semantic tree based on the semantic element parsing result by employing a target structured rule associated with the target semantic element rule; and performing semantic understanding on the text to be parsed based on the semantic tree.

Abstract: The present application discloses a method, an apparatus, a device, and a computer readable storage medium for determining a target content, the method includes: splitting an article paragraph determined according to search information into multiple sentences, and determining a relationship between the sentences according to attributes of the sentences; determining a sentence representation corresponding to each of the sentences according to the relationship between the sentences; and determining a target sentence according to the sentence representation of the sentence and the search information, and determining a target content according to the target sentence, so that the method, the apparatus, the device, and the computer readable storage medium provided by the present disclosure can analyze each of the sentences in combination with the relationship between the sentences, thereby determining a target content that more closely matches the search information.