Abstract: The present disclosure relates to a method and an apparatus based on a neural network model. The method includes acquiring a plurality of training samples, each of the training samples comprising an identifier of an input word, an identifier of an output word, and position information, wherein in one of the training samples, the output word is a context of the input word, and the position information indicates a relative position between the output word and the input word; calling a position relation-based Continuous Skip-gram Model (Skip-Gram), with each of the training samples as an input, to obtain an output result, the output result comprising a word matrix embedding of the input word, a word matrix embedding of the output word and a position matrix embedding of the position information; and updating the position relation-based Skip-Gram based on the output result to train the position relation-based Skip-Gram.

Abstract: A text processing method includes: word segmentation is performed on a first text to be processed to determine multiple words of the first text; one word is removed from the first text to obtain one second text; each word is removed from the first text one by one to obtain a second text set; the first text and each second text in the second text set are analyzed respectively to obtain a first tree structure of the first text and a second tree structure of each second text; and a summary text of the first text is determined according to the first tree structure and each second tree structure.

Abstract: A method for processing untagged data includes: similarity comparison is performed on a semantic vector of untagged data and a semantic vector of each piece of tagged data to obtain similarities corresponding to respective pieces of tagged data; a preset number of similarities are selected according to a preset selection rule; the untagged data is predicted with a tagging model obtained by training through the tagged data, to obtain a prediction result of the untagged data; and the untagged data is divided into untagged data that can be tagged by a device or untagged data that cannot be tagged by the device according to the preset number of similarities and the prediction result.

Abstract: A method for processing untagged data includes: similarity comparison is performed on a semantic vector of untagged data and a semantic vector of each piece of tagged data to obtain similarities corresponding to respective pieces of tagged data; a preset number of similarities are selected according to a preset selection rule; the untagged data is predicted with a tagging model obtained by training through the tagged data, to obtain a prediction result of the untagged data; and the untagged data is divided into untagged data that can be tagged by a device or untagged data that cannot be tagged by the device according to the preset number of similarities and the prediction result.

Abstract: A text processing method includes: word segmentation is performed on a first text to be processed to determine multiple words of the first text; one word is removed from the first text to obtain one second text; each word is removed from the first text one by one to obtain a second text set; the first text and each second text in the second text set are analyzed respectively to obtain a first tree structure of the first text and a second tree structure of each second text; and a summary text of the first text is determined according to the first tree structure and each second tree structure.

Abstract: The present disclosure relates to a method and an apparatus based on a neural network model. The method includes acquiring a plurality of training samples, each of the training samples comprising an identifier of an input word, an identifier of an output word, and position information, wherein in one of the training samples, the output word is a context of the input word, and the position information indicates a relative position between the output word and the input word; calling a position relation-based Continuous Skip-gram Model (Skip-Gram), with each of the training samples as an input, to obtain an output result, the output result comprising a word matrix embedding of the input word, a word matrix embedding of the output word and a position matrix embedding of the position information; and updating the position relation-based Skip-Gram based on the output result to train the position relation-based Skip-Gram.