The invention discloses a role determination method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technicalfield of deep learning and natural language processing. The specific implementation scheme is as follows: constructing a corresponding target sample based on a target dialogue text in a specified text; predicting position information of a role corresponding to the target dialogue text by using a pre-trained role prediction model based on the target sample; and determining a corresponding role based on the position information of the role and the target sample. According to the technical scheme, the roles of the dialogue text in the text can be determined, then the roles of the dialogue text can be marked, an effective basis is provided for multi-role audio reading of the AI, and large-scale operation of multi-role audio reading of the AI can be facilitated.

Role determination method and device, electronic equipment and storage medium

The invention provides a false news prediction method based on a relational network and an attention mechanism. The method comprises the following steps: firstly, obtaining a text feature vector and arelational network feature vector of news; fusing the text feature vector and the relational network feature vector through a two-layer neural network to obtain a news prediction function; and finally, optimizing the classification prediction model in combination with a news prediction function to obtain a final prediction classification function of news. According to the method, on the basis ofintroducing multi-source and multi-type features into prediction of false news, a relationship network feature vector is obtained by obtaining a news edge information relationship network structure, and the relationship network feature vector is fused with a news text feature vector to obtain a news prediction function; an attention mechanism is introduced into the acquisition of the relationshipnetwork feature vector, and a more accurate relationship network feature vector is acquired through attention weight; a better news prediction effect is obtained by optimizing a plurality of tasks ina multi-task training mode.

False news prediction method based on relational network and attention mechanism

The invention provides a false news prediction method, system and device based on deep learning and a medium, which can increase the prediction speed and can avoid the defect of long-term dependence.The method comprises the following steps: 1, preprocessing news text data; 2, mapping the preprocessed news text data into word vectors by using a BERT pre-training model; 3, constructing a predictionmodel based on the deep learning neural network, wherein the constructed prediction model comprises a feature extraction layer, an attention layer and a classifier layer; 4, obtaining an optimized prediction model through adversarial training; 5, inputting the training data into the optimization prediction model, and training the prediction model to obtain a trained prediction model; 6, inputtingnews text data to be predicted into the trained prediction model, and outputting a prediction result.

False news prediction method, system and device based on deep learning, and medium

The invention provides a Russian semantic role labeling method, system and device and a storage medium. Aiming at the characteristics of Russian, the relation between predicates and arguments is reasonably utilized, the semantic role annotation of the Russian can be well realized, the semantic role annotation accuracy is improved, and higher annotation performance is obtained. The method comprisesthe following steps: 1, preprocessing corpora, extracting classification characteristics, and converting the classification characteristics into characteristic vectors; 2, constructing classificationmodels based on neural networks of different architectures, and inputting classification features into the classification models for training to obtain trained classification models; 3, based on a voting fusion mechanism, fusing the trained classification models according to the principle that a small number obeys a majority to obtain a fusion model; 4, inputting the preprocessed corpus into a fusion model, identifying a semantic role, attaching a prediction label, and performing performance evaluation on an obtained semantic role prediction result.

Russian semantic role labeling method, system and device and storage medium

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False news prediction method, system and device based on deep learning, and medium

Russian semantic role labeling method, system and device and storage medium