Abstract: Disclosed are a material analysis method based on the crystal structure database, a system, a computer-readable storage medium and an application. The material analysis method includes comparing experimental pattern information obtained from examination of a to-be-tested sample with theoretical pattern information calculated from material structure data in the crystal structure database, and obtaining crystallographic information and phase composition of the to-be-tested sample through intelligent analysis. The crystallographic information include space group, unit cell parameter, and specific coordinates of atoms in unit cell. The crystal structure database has material structure data obtained by experimental measurement and/or theoretical prediction, including chemical formula, space group, unit cell parameter and specific coordinates of atoms in unit cell.

Abstract: A method for retrieving a multi-turn dialogue, a storage medium, and an electronic device are provided. The detailed implementation includes: obtaining present question information and context information of the present question information; obtaining a plurality of pieces of candidate reply information of the present question information; generating a context feature based on the context information, and generating a plurality of candidate reply features based on the plurality of pieces of candidate reply information; obtaining consistency information between the context feature and each of the plurality of candidate reply features; selecting a piece of candidate reply information from the plurality of pieces of candidate reply information for replying based on the consistency information between the context feature and each of the plurality of candidate reply features.

Abstract: A method for retrieving a multi-turn dialogue, a storage medium, and an electronic device are provided. The detailed implementation includes: obtaining present question information and context information of the present question information; obtaining a plurality of pieces of candidate reply information of the present question information; generating a context feature based on the context information, and generating a plurality of candidate reply features based on the plurality of pieces of candidate reply information; obtaining consistency information between the context feature and each of the plurality of candidate reply features; selecting a piece of candidate reply information from the plurality of pieces of candidate reply information for replying based on the consistency information between the context feature and each of the plurality of candidate reply features.

Abstract: An information extraction method, an extraction model training method, an apparatus and an electronic device all relate to knowledge graphs. A specific implementation includes acquiring an input text and determining a semantic vector of the input text according to the input text. Such implementation also includes inputting the semantic vector of the input text to a pre-acquired extraction model to obtain a first enhanced text of the input text. The first enhanced text is a text with a text score greater than a preset threshold output by the extraction model. The extraction model performs text extraction based on the semantic vector of the input text. Since the semantic vector has rich context semantics, the enhanced text extracted by the extraction model can be more in line with the context of the input text.