Abstract: The present application relates to the technical field of computers, and discloses a method and apparatus for generating a music file, and an electronic device and a storage medium. The method for generating a music file comprises: obtaining a first image; performing feature extraction on the first image to obtain a salient feature of the first image; mapping the salient feature to a musical instrument digital interface (MIDI) information coordinate system on the basis of the position of the salient feature in the first image, so as to determine MIDI information corresponding to the salient feature, the MIDI information coordinate system being used for indicating a correspondence between MIDI information and time; and generating a music file on the basis of the correspondence between the MIDI information and the time.

Abstract: Embodiments of the present disclosure provide a method and a device for music play. The method comprises receiving a first operation instruction in a target application for playing music; in response to the first operation instruction, presenting a first interface of the target application, the first interface including an operation control for enhancing a play effect of the music through at least one processing, the processing being used for representing music content in a way more than sound; receiving a second operation instruction for the operation control; processing the music based on the second operation instruction during a process of playing the music.

Abstract: A method for natural language processing includes receiving, by one or more processors, an unstructured text input. An entity classifier is used to identify entities in the unstructured text input. The identifying the entities includes generating, using a plurality of sub-classifiers of a hierarchical neural network classifier of the entity classifier, a plurality of lower-level entity identifications associated with the unstructured text input. The identifying the entities further includes generating, using a combiner of the hierarchical neural network classifier, a plurality of higher-level entity identifications associated with the unstructured text input based on the plurality of lower-level entity identifications. Identified entities are provided based on the plurality of higher-level entity identifications.

Abstract: A method and apparatus include receiving a first sentence including a first set of words, and a second sentence including a second set of words. A first set of vectors corresponding to the first set of words of the first sentence, and a second set of vectors corresponding to the second set of words of the second sentence are generated using a word embedding model. A similarity matrix based on the first set of vectors and the second set of vectors is generated. An alignment score associated with the first set of vectors and the second set of vectors is determined using the similarity matrix. The alignment score is transmitted to permit information retrieval based on a similarity between the first sentence and the second sentence.

Abstract: A method and apparatus include receiving a first sentence including a first set of words, and a second sentence including a second set of words. A first set of vectors corresponding to the first set of words of the first sentence, and a second set of vectors corresponding to the second set of words of the second sentence are generated using a word embedding model. A similarity matrix based on the first set of vectors and the second set of vectors is generated. An alignment score associated with the first set of vectors and the second set of vectors is determined using the similarity matrix. The alignment score is transmitted to permit information retrieval based on a similarity between the first sentence and the second sentence.

Abstract: A method for natural language processing includes receiving, by one or more processors, an unstructured text input. An entity classifier is used to identify entities in the unstructured text input. The identifying the entities includes generating, using a plurality of sub-classifiers of a hierarchical neural network classifier of the entity classifier, a plurality of lower-level entity identifications associated with the unstructured text input. The identifying the entities further includes generating, using a combiner of the hierarchical neural network classifier, a plurality of higher-level entity identifications associated with the unstructured text input based on the plurality of lower-level entity identifications. Identified entities are provided based on the plurality of higher-level entity identifications.