Method for automatically classifying academic documents

Abstract

The invention discloses a method for automatically classifying academic documents, which comprises the following steps: inputting training documents into a database, wherein the training documents comprise document classification numbers; selecting unitary characteristic words and binary characteristic words, and generating binary word pairs for the training documents; reading the training documents in the database, and respectively calculating the probability relations between the unitary and binary characteristic words and the document classification numbers, thereby forming a unitary classification dictionary and a binary classification dictionary; reading a document to be labelled, calculating the Chinese library classification number corresponding to the document according to the weight of the unitary and binary classification dictionaries, and the unitary and binary characteristic words in the document to be labelled, and automatically labelling; and dividing the unitary and binary classification result into a high-accuracy result set and a low-accuracy result set according to the degree of confidence, and outputting the classification result.

Description

technical field [0001] The invention relates to a method for classifying academic documents, in particular to a method for automatically classifying academic documents. Background technique [0002] With the development of information technology, Internet data and resources present massive characteristics. In order to effectively manage and utilize these distributed massive information, content-based information retrieval and data mining have gradually become areas of concern. Among them, text classification (text categorization , referred to as TC) technology is an important basis for information retrieval and text mining, and its main task is to determine its category according to the text content under the pre-given category label (1abel) set. [0003] Text classification has a wide range of applications in natural language processing and understanding, information organization and management, content information filtering and other fields. The text classification method...

AI Technical Summary

Problems solved by technology

[0013] 1. The acquisition of classification numbers mainly relies on the manual review of editors, which not only consumes a lot of manpower and material resources, but also has low efficiency; a large number of people are engaged in repetitive labor all year round, resulting in serious waste of resources

[0014] 2. Manual classification standards are not uniform, and interdisciplinary articles are prone to ambiguity in classification

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