A classifier construction method and classifier based on semantic computing

Abstract

The invention belongs to the technical field of information retrieval and its database structure, and discloses a semantic calculation-based classifier construction method and a classifier, which use a neural network model to train word vectors on Wikipedia data to obtain distributed representations of words; through classification The vector representation of the classification is obtained by the label, and the vector representation of the text is obtained by using the weighted average method; the most likely classification of the text is obtained by calculating the semantic relationship between the classification vector and the text vector. The unsupervised learning stage of the present invention does not need to label data, only needs the user to define a small number of feature words to complete the creation of the classifier, the online speed is fast, and there is no need to wait for the long accumulation of label data; the unsupervised learning stage can make full use of the existing limited labels Data, guide and improve unsupervised classification ability by extracting effective feature words.

Description

technical field [0001] The invention belongs to the technical field of information retrieval and database structure thereof, and in particular relates to a semantic calculation-based classifier construction method and a classifier. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: With the continuous deepening of the globalization process and the rapid development of the Internet, text data is showing explosive growth, but the data sources and forms are diverse, which brings great challenges to the management and use of documents. Text classification technology uses machine learning methods to automatically classify and mark text sets according to a certain classification system or standard, so as to realize the classification, archiving and fast query and retrieval of massive data. At present, text classification technology is relatively mature and has been widely used in many fields. The most primitive metho...

AI Technical Summary

Problems solved by technology

[0004] (1) Manual labeling of data often requires heavy manual labor and requires high labeling accuracy, which often requires three people to label the same text, resulting in long labeling work cycles, high labor costs, and slow production speed;

[0005] (2) In the absence of corpus, the model trained by the classifier is poor, which often cannot meet the effectiveness requirements of the system application

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