The invention relates to the field of AI, in particular to a data annotation correction method and device, a computer readable medium and electronic equipment. The data annotation correction method includes the steps: adopting a plurality of learning models with different expression capabilities to calculate the classification score of each piece of sample data in a training set at the same time;and selecting the sample data of which the classification score under each learning model is within the threshold range corresponding to the learning model as the sample data to be corrected, and taking the sample data with the highest classification annotation proportion calculated by the plurality of learning models as the annotation of the sample data. Therefore, the data annotation correctionmethod does not need to perform annotation correction on all sample data in the training set, can automatically check the sample data, finds out the sample data set which is most likely to have annotation errors, automatically and accurately corrects annotations in the sample data set, replaces manual annotation, and has the characteristics of high efficiency and low cost.

Data annotation correction method and device, computer readable medium and electronic equipment

The embodiment of the invention discloses a model training method, an information generation method and device, electronic equipment and a computer readable medium. The embodiment of the model training method comprises the following steps: extracting a sample text and a tag word sequence of the sample text from a sample set; Determining a first word vector sequence corresponding to the sample textand a second word vector sequence corresponding to the tag word sequence; Taking the first word vector sequence as the input of a pre-established first initial model; taking the output of the first initial model and the first word vector sequence as the input of a pre-established second initial model, taking the second word vector sequence as the output of the second initial model, and training the first initial model and the second initial model by using a machine learning method; And determining the trained first initial model as a semantic vector generation model, and determining the trained second initial model as a tag word generation model. According to the embodiment, the tag word generation accuracy is improved.

Model training method, information generation method and device, electronic equipment and computer readable medium

The invention provides a similarity model training method and device based on multiple sensors and multiple tested objects. The method comprises the following steps of: obtaining a target object, thedetection result of each sensor is obtained; obtaining a marking result of each tested object, for variable detection results, calculating a first similarity between the corresponding detection resultand each labeling result; depending on the first similarity, determining a second similarity between different detection results, according to a second similarity between different detection results;generating a positive sample of the similarity detection result combination and a negative sample of the non-similarity detection result combination; the similarity model is trained by adopting the positive samples and the negative samples, the positive samples of the similar result combinations and the negative samples of the non-similar result combinations are determined by calculating the similarity among the detection results of the sensors, and the model is trained based on the positive samples and the negative samples, so that the accuracy of the similarity model obtained through training is improved.

Similarity model training method and device based on multiple sensors and multiple tested objects

A vector representation generation method, apparatus and device for a knowledge graph, wherein same relate to the technical field of artificial intelligence. The specific implementation solution is: acquiring a knowledge graph, wherein the knowledge graph comprises a plurality of entity nodes (101); acquiring a context type and context data corresponding to the knowledge graph (102); and generating, according to the context type and the context data and by means of a context model, a vector representation corresponding to the plurality of entity nodes (103). Thus, finer semantic representation of an entity in context is realized, thereby further improving the accuracy of knowledge graph representation learning.

Vector representation generation method, apparatus and device for knowledge graph

The invention relates to the field of medical science and technology, and particularly discloses a medical text translation method and device and a storage medium. The method comprises the steps of obtaining a to-be-translated medical text; performing semantic feature extraction on the to-be-translated medical text to obtain a first feature vector; obtaining a target feature vector corresponding to the to-be-translated medical text, wherein the target feature vector is used for representing a medical knowledge graph corresponding to the to-be-translated medical text; splicing the first featurevector and the target feature vector to obtain a second feature vector; and translating the to-be-translated medical text according to the second feature vector. The embodiment of the invention is beneficial to improving the accuracy of medical text translation.

Medical text translation method and device and storage medium

The invention provides a mixed annotation learning neural network model, and a training method and device of the model. The model comprises a first sub neural network model and a second sub neural network model, wherein the first sub neural network model is used for encoding and decoding input data to be processed to generate initial result representation corresponding to the data to be processed,and the second sub neural network model is used for correcting the initial result representation corresponding to the data to be processed according to a vector corresponding to an acquired processing standard to generate target result representation corresponding to the data to be processed. According to the mixed annotation learning neural network model, training corpuses with different annotation standards are integrally used, the adaptability of the neural network model to the difference between the training corpuses with different annotation standards is improved, universality of the neural network model is enhanced, and processing performance is improved.

Mixed annotation learning neural network model, and training method and device of model

The invention discloses a text processing method and device based on a natural language and a knowledge graph, and relates to the technical field of artificial intelligence. According to the specificimplementation scheme, an electronic device uses semantic representation obtained by a joint learning model, the joint learning model is obtained by combining knowledge graph representation learning and natural language representation learning and combines knowledge graph learning representation and natural language learning representation; compared with semantic representation of learning a prediction object only through knowledge graph representation learning or natural language representation learning, the joint learning model considers more and more comprehensive factors, and therefore theaccuracy of semantic representation can be improved, and the accuracy of text processing is improved.

Text processing method and device based on natural language and knowledge graph

The invention provides a knowledge graph vector representation generation method, device and equipment, and relates to the technical field of artificial intelligence, and the specific implementation scheme is that the method comprises the steps: obtaining a knowledge graph, and enabling the knowledge graph to comprise a plurality of entity nodes; obtaining a context type and context data corresponding to the knowledge graph; and generating vector representations corresponding to the plurality of entity nodes through a context model according to the context data and the context type. Therefore,finer semantic representation of the entity in the context is realized, and the knowledge graph representation learning accuracy is further improved.

Wang Haifeng

Papers

Mixed annotation learning neural network model, and training method and device of model

Text processing method and device based on natural language and knowledge graph

Knowledge graph vector representation generation method, device and equipment

Vector representation generation method, apparatus and device for knowledge graph