A method for recognizing network text named entity based on neural network probability disambiguation. The method comprises: carrying out word segmentation on an unlabeled corpus, using Word2Vec to extract a word vector; converting a sample corpus into a word feature matrix and windowing same; building a deep neural network to carry out training, and adding a softmax function into an output layer of the neural network to carry out normalization processing, so as to obtain a probability matrix of the named entity category corresponding to each word; and re-windowing the probability matrix, and using a conditional random field model to carry out disambiguation, so as to obtain a final named entity annotation. In a named entity recognition task of network text, a word vector increment learning method without changing the structure of a neural network is provided, according to the characteristic that a network vocabulary and a new vocabulary exist therein, and a probability disambiguation method is used in order to deal with the problems of a nonstandard grammatical structure and many wrongly written characters in the network text. Therefore, higher accuracy can be produced.
METHOD FOR RECOGNIZING NETWORK TEXT NAMED ENTITY BASED ON NEURAL NETWORK PROBABILITY DISAMBIGUATION