Text classification method for open network questions in specific field
The invention belongs to the technical field of text classification processing, and particularly relates to a text classification method for open network questions in a specific field. According to the method, the problems of lack of enough available corpus sets with category marks, low network text...
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| creator | YU RICHANG YANG HUI LI RONGSHENG LIU WANGYANG ZHANG BAIJIA HUANG SHAOBIN SHEN LINSHAN LI YI |
| description | The invention belongs to the technical field of text classification processing, and particularly relates to a text classification method for open network questions in a specific field. According to the method, the problems of lack of enough available corpus sets with category marks, low network text information amount and high noise under the condition of executing network open text classificationtasks in certain specific fields are solved, and a new method is provided for hierarchical classification of open network questions in the fields. According to the method, open network questions andwritten texts in a specific domain are utilized to enable word embedding representation in the domain to better conform to domain knowledge features, and meanwhile, a semi-supervised method is used for accelerating classification model training and reducing required marked samples; and in addition, category classification at a multi-granularity level is realized in combination with conditional probability. The method can a |
| format | Patent |
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According to the method, the problems of lack of enough available corpus sets with category marks, low network text information amount and high noise under the condition of executing network open text classificationtasks in certain specific fields are solved, and a new method is provided for hierarchical classification of open network questions in the fields. According to the method, open network questions andwritten texts in a specific domain are utilized to enable word embedding representation in the domain to better conform to domain knowledge features, and meanwhile, a semi-supervised method is used for accelerating classification model training and reducing required marked samples; and in addition, category classification at a multi-granularity level is realized in combination with conditional probability. 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | Text classification method for open network questions in specific field |
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