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: Systems and methods for obtaining a panoramic image are provided. One system includes a gantry, an x-ray source, a receptor, and at least one controller. The x-ray source is mounted on the gantry and is configured to alternatively output x-ray radiation at a first energy level and x-ray radiation at a second energy level. The receptor is mounted on the gantry so that x-ray radiation from the x-ray source impinges on the receptor. The receptor is configured to output a plurality of frames of data including a first frame and a second frame sequential to the first frame. The controller is configured to control the x-ray source so that data in the first frame is generated based on x-ray radiation of the first energy level and data in the second frame is based on x-ray radiation of the second energy level.

Abstract: Systems and methods for obtaining a panoramic image. One system includes a gantry, an x-ray source, a receptor, and at least one controller. The x-ray source is mounted on the gantry and is configured to alternatively output x-ray radiation at a first energy level and x-ray radiation at a second energy level. The receptor is mounted on the gantry so that x-ray radiation from the x-ray source impinges the receptor. The receptor is configured to output a plurality of frames of data including a first frame and a second frame sequential to the first frame. The controller is configured to control the x-ray source so that data in the first frame is generated based on x-ray radiation of the first energy level and data in the second frame is based on x-ray radiation of the second energy level.

Abstract: A method and a system for generating an image by obtaining x-ray image data, segmenting the x-ray image data into a first portion above a vertical threshold and a second portion below the vertical threshold. Further, the method and the system include generating an arch for the second plurality of slices, and generating an image based on the arch.

Abstract: A method and a system for generating a panoramic x-ray image by obtaining volumetric x-ray image data having a first plurality of slices, segmenting the x-ray image data into a first portion above a vertical threshold and a second portion below the vertical threshold, and separating the second portion into a second plurality of slices. Further, the method and the system include generating a plurality of curves for each slice in the second plurality of slices, generating a master arch for the second plurality of slices, and generating a panoramic image based on the master arch.

Abstract: A method and a system for generating a panoramic x-ray image by obtaining volumetric x-ray image data having a first plurality of slices, segmenting the x-ray image data into a first portion above a vertical threshold and a second portion below the vertical threshold, and separating the second portion into a second plurality of slices. Further, the method and the system include generating a plurality of curves for each slice in the second plurality of slices, generating a master arch for the second plurality of slices, and generating a panoramic image based on the master arch.