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A Book Automatic Classification Method Based on LDA Topic Model

A topic model and automatic classification technology, applied in the field of information, can solve problems such as inability to achieve results, and achieve the effect of reducing errors and accurate identification

Active Publication Date: 2020-10-16
EB INFORMATION TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since books are essentially a collection of texts, classified books can include both online literature and traditional literature, and the above method does not achieve very good results

Method used

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  • A Book Automatic Classification Method Based on LDA Topic Model
  • A Book Automatic Classification Method Based on LDA Topic Model
  • A Book Automatic Classification Method Based on LDA Topic Model

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Embodiment Construction

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0022] Such as figure 1 Shown, a kind of book automatic classification method based on LDA subject model of the present invention comprises:

[0023] Step 1. Establish a classification system containing K categories;

[0024] Step 2. Select books of known categories as training books, and extract book tags from each training book. The book tags of all training books constitute the total set of book tags, and assign a unique book tag to each book tag in the total set of book tags serial number;

[0025] Step 3. Taking the training books as samples, construct and train a multinomial distribution model. The input of the multinomial distribution model is all the book labels contained in each training book and the categories of the training books, and the output is ...

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Abstract

The invention discloses a book automatic classification method based on an LDA topic model. The method comprises the steps : a classification system is established; Books of known categories are selected as training books, labels of all the training books form a book label general set, and a unique serial number is distributed to each label in the book label general set; A multi-item distributionmodel is constructed and trained, the input of the multi-item distribution model is the book label contained in the training book and the training book category, and the output of the multi-item distribution model is the probability of each label in the book label total set under different categories; book labels are selected from the books to be classified; a label set of the to-be-classified book is formed, then a Gibbs sampling method is adopted to distribute a category for each book label sample contained in the to-be-classified book based on an LDA topic model, the score of each categoryto which the to-be-classified book belongs is counted when convergence is achieved, and thus obtaining the category to which the to-be-classified book belongs. The invention belongs to the technicalfield of information, and can realize automatic book classification based on an LDA topic model.

Description

technical field [0001] The invention relates to an automatic book classification method based on an LDA topic model, which belongs to the field of information technology. Background technique [0002] Book classification has always been of great significance to online and offline library institutions with large collections. For online literature platforms and online bookstores respected by emerging reading groups, accurate book classification is the basis for accurate recommendation of various books. For libraries and physical bookstores that carry traditional published literature, accurate book classification can improve management efficiency and improve user experience. experience. For these institutions, because there are many old books that need to be corrected and new books that are constantly on the shelves, the current manual-based book classification method has problems such as heavy workload, low efficiency, subjective classification, and inaccuracy. Therefore, the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/35G06F40/258
Inventor 符俊涛王超芸李曲应文佳马堃沈钦壮
Owner EB INFORMATION TECH