Dynamic topic model-based dynamic text cluster device and method

Abstract

The invention discloses a dynamic topic model-based dynamic text cluster device and method. The device comprises a news acquisition module, a news initial feature extracting module, a dynamic feature converting module and a dynamic cluster module, wherein the news acquisition module is used for acquiring news data in the Internet; the news initial feature extracting module is used for extracting the initial feature of the acquired news data; the dynamic feature converting module is used for carrying out dynamic feature conversion on the extracted initial feature; and the dynamic cluster module is used for carrying out dynamic cluster on a news data set based on the converted feature. With the adoption of the device and the method, the problem of effective cluster of the dynamic text data set is solved so as to combine a dynamic topic model with a dynamic cluster algorithm, and the inherent problems of polysemy, multiple words for one meaning, data sparsity, feature dimension disaster and the like of the text feature based on a term are solved.

Description

technical field [0001] The invention relates to machine learning and pattern recognition technology, in particular to a dynamic text clustering device and method based on a dynamic topic model. Background technique [0002] With the explosive development of information technology, the text to be processed by computers is increasing rapidly. Text clustering is a common text processing method, which gathers similar texts from a certain point of view, that is, it may be used as a direct output information, or it may be used as the basis for further processing of the text. important meaning. [0003] Most texts are described in natural language. To perform clustering, features must be extracted and converted into quantitative descriptions. However, conventional feature extraction methods are difficult to avoid problems such as data sparsity, high feature dimension, one word with multiple meanings, and one meaning with multiple words, which will have a great adverse effect on...

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Problems solved by technology

[0004] In view of this, the main purpose of the present invention is to provide a dynamic text clustering device and method based on a dynamic topic model to solve the problem of effective clustering of dynamic text data sets, so as to realize the combination of dynamic topic models and dynamic clustering algorithms , to solve the problems inherent in traditional term-based text features such as one word with multiple meanings, one meaning with multiple words, data sparseness, feature dimension disaster, etc.

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