Short text classification method based on part-of-speech and fuzzy pattern recognition combination

A technology of fuzzy pattern recognition and classification method, applied in text database clustering/classification, unstructured text data retrieval, special data processing applications, etc. Efficiency improvement effect

Active Publication Date: 2019-05-24
SICHUAN CHANGHONG ELECTRIC CO LTD
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Problems solved by technology

At present, there is also a method based on corpus expansion for short text classific

Method used

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  • Short text classification method based on part-of-speech and fuzzy pattern recognition combination
  • Short text classification method based on part-of-speech and fuzzy pattern recognition combination
  • Short text classification method based on part-of-speech and fuzzy pattern recognition combination

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Embodiment

[0029] Such as figure 1 As shown, a short text classification method based on the combination of speech and fuzzy pattern recognition, such as figure 1 As shown, it specifically includes the following processes:

[0030] Step 1: Divide the request text data with correct field classification into different fields, and record the field set as D={d 1 , d 2 ,...,d n};

[0031] As in the present embodiment, the correct request text data of the field classification is divided into different fields, as the user's request text to the smart TV is divided into four fields of VIDEO, TV, MUSIC and APP, then D={VIDEO, TV, MUSIC ,APP}.

[0032] Step 2: Extract high-frequency feature words from the text data with correct domain classification through different parts of speech as the basic domain features of the domain, and extract entities from the relevant knowledge graphs of the domain as the extended domain features of the domain.

[0033] In this embodiment, according to different ...

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Abstract

The invention discloses a short text classification method based on part-of-speech and fuzzy pattern recognition combination. for a user request text with failed domain classification, extracting feature words with different parts of speech from the historical data with correct domain classification to form basic domain features, and extracting entities in related domains in combination with a knowledge graph to form extended domain features; performing extended part-of-speech tagging on the basic domain feature and the extended domain feature to form a user-defined dictionary; pn the basis ofthe thought of first coarse classification and then subdivision, part-of-speech mode matching and the maximum membership degree principle are combined to carry out domain classification on the to-be-classified text, and finally acquiring a short text classification result with high accuracy. The method provided by the invention can be used for performing domain classification on the user requesttext in the human-computer interaction process, so that the accuracy and efficiency of short text classification are improved.

Description

technical field [0001] The invention relates to the technical field of computer natural language processing, in particular to a short text classification method based on a combination of part-of-speech and fuzzy pattern recognition. Background technique [0002] With the rapid development of computer technology and the wide application of various smart devices, more and more intelligent customer services appear in our lives. People can interact with smart devices through simple voice input. First, the user's voice information is converted into a request text, and then the request text is analyzed to obtain the result, and finally the text data that has been successfully parsed is transmitted to the terminal device for subsequent processing. In order to better analyze the user's request text, it is particularly important to classify the text. [0003] Currently commonly used text classification algorithms include naive Bayesian algorithm, KNN algorithm, support vector machin...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06F17/27
Inventor 唐军杜忠和刘楚雄
Owner SICHUAN CHANGHONG ELECTRIC CO LTD
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