首页> 外国专利> METHOD OF RECOGNIZING NAMED ENTITIES IN NETWORK TEXT BASED ON ELIMINATION OF PROBABILITY AMBIGUITY IN NEURAL NETWORK

METHOD OF RECOGNIZING NAMED ENTITIES IN NETWORK TEXT BASED ON ELIMINATION OF PROBABILITY AMBIGUITY IN NEURAL NETWORK

机译:基于神经网络概率模糊消除的网络文本中命名实体识别方法

摘要

FIELD: computer equipment.;SUBSTANCE: invention relates to computer engineering. Disclosed is a method of recognizing named entities of network text based on eliminating ambiguity of probability in a neural network, involving: performing word decomposition on unmapped text body using Word2Vec model to extract word vector, converting reference text bodies into a word feature matrix, performing window processing, constructing a deep neural network for training, adding a Softmax function to the output layer of the neural network and performing normalization to obtain a matrix of probabilities of the category of named entities corresponding to each word; performing repeated processing of the probability matrix by the window method and using the model of conditional random fields to eliminate ambiguity to obtain the final tag of the named entity.;EFFECT: enabling recognition of named entities of network text based on elimination of probability ambiguity in a neural network.;7 cl, 3 dwg
机译:技术领域本发明涉及计算机工程。公开了一种基于消除神经网络中的概率歧义来识别网络文本的命名实体的方法,该方法包括:使用Word2Vec模型对未映射的文本主体进行词分解,以提取词向量,将参考文本主体转换为词特征矩阵,执行窗口处理,构造用于训练的深层神经网络,向该神经网络的输出层添加Softmax函数,并进行归一化,以获得与每个单词相对应的命名实体类别的概率矩阵;通过窗口方法并使用条件随机字段模型消除歧义性来重复处理概率矩阵,以获得命名实体的最终标签。;效果:基于在网络中消除概率歧义性来识别网络文本中的命名实体神经网络。; 7 cl,3 dwg

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