Chinese news subject collaborative segmentation method based on probabilistic graphical model

A probabilistic graph model and collaborative segmentation technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as large changes in production rules

Inactive Publication Date: 2013-05-15
TIANJIN UNIV
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  • Chinese news subject collaborative segmentation method based on probabilistic graphical model
  • Chinese news subject collaborative segmentation method based on probabilistic graphical model
  • Chinese news subject collaborative segmentation method based on probabilistic graphical model

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Embodiment Construction

[0030] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. If there are exemplary contents in these embodiments, they should not be construed as limiting the present invention.

[0031] The specific embodiment of the present invention mainly comprises the following steps:

[0032] Step 1. Establishment and initialization of the graph model

[0033] 1. Establishment of the graph model

[0034] Taking the input of two Chinese news story script documents as an example, a graph model (graph) with pseudo-sentence as nodes is constructed. The specific method is as follows: firstly, the input document is divided into a series of pseudo-sentences according to the fixed length, and these pseudo-sentences are mapped into nodes in the graph; secondly, the edges in the graph model are composed of two parts of edges, and some of the edges are composed of the same The adjacent nodes in the document (that is, adjace...

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Abstract

The invention discloses a Chinese news subject collaborative segmentation method based on a probabilistic graphical model. The Chinese news subject collaborative segmentation method based on the probabilistic graphical model comprises steps as below: step one: constructing a graph model, initializing a foreground subject model and a background subject model of an inputted unreliable Chinese news text document; step two: revising the foreground subject model and the background subject model; step three: constructing an energy equation; step four: solving the answer of the energy equation in an optimized mode, and obtaining a collaborative segmentation result. The Chinese news subject collaborative segmentation method based on the probabilistic graphical model directly uses relevance among data to achieve the semantic level segmentation effect, can greatly help to make up the gap between the bottom characteristics and the high level semantics, at the same time, is beneficial for improving reliability and processing capability of extraction and segmentation of a large amount of unreliable text-type Chinese news story subjects, capable of improving accuracy and generality of segmentation of the large amount of unreliable text-type Chinese news stories, and therefore facilitating automatic analyzing and processing of the Chinese news story subjects.

Description

technical field [0001] The present invention relates to the field of text segmentation and topic extraction, in particular to a new technology of topic co-segmentation oriented to Chinese news. Background technique [0002] The background technology involved in the present invention has: [0003] (1) Chinese news story segmentation (Story Segmentation): For the segmentation of Chinese news stories, previous techniques mainly focus on the establishment of topic models and the selection of topic boundary clues. Common topic models such as Hidden Markov Model, Exponential Model, Maximum Entropy Model, etc. Commonly used topic boundary clues include video clues, audio clues and text clues. Among them, the switching of scenes or hosts can be used as video clues, and obvious pauses or changes of speakers can be used as audio clues. But both video and audio cues rely to some extent on the rules of news production and editing. In different media files, these authoring rules may ...

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Application Information

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IPC IPC(8): G06F17/27G06F17/24
Inventor 冯伟万亮聂学成
Owner TIANJIN UNIV
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