Abstract: An embodiment of the present application provides a text categorization method and apparatus, after obtaining a first text set, performing multiple clustering on the texts in the plurality of text pairs, and obtaining a second text set in each clustering; and for each text pair, calculating a clustering probability that all the texts in the text pair are clustered into the same second text set in the multiple clustering; and then categorizing the first text set into a plurality of third text sets based on the clustering probability of each text pair; in this way, a preliminary clustering is performed on the to-be-categorized texts, and then an accurate categorization is performed in combination with a preliminary clustering result to obtain a final text categorization result, not directly taking the text clustering result as the final text categorization result, which can improve the accuracy of text categorization.

Abstract: The present disclosure provides a domain dictionary constructing method and apparatus, the method including: segmenting a domain corpus sample to obtain a first character segment set, where the first character segment set includes at least one first character segment; calculating an inter-character correlation index of the at least one first character segment; determining a second character segment set according to the correlation index, where the second character segment set includes a first character segment of which the correlation index is greater than or equal to a preset threshold; determining a third character segment set according to the second character segment set, where the third character segment set includes the second character segment set and second character segments of the domain corpus; constructing a domain dictionary according to the third character segment set.

Abstract: An embodiment of the present application provides a text segmentation method, a computer device and a storage medium, among them, the method includes: obtaining a text to be segmented, preprocessing the text and determining a processed text; calculating a confidence of a segment point between two adjacent characters in the processed text, determining a position of the segment point in the processed text based on the confidence; and segmenting the text according to the position. Through this embodiment, the problem of low efficiency of word segmentation in existing word segmentation method can be solved.