"Text classification" is an important application of natural language processing, and it can also be said to be the most basic application. Text classification uses computers to automatically classify and label text sets according to a certain classification system or standard; it finds the relationship model between document features and document categories based on a set of tagged training documents, and then uses this relationship model to classify new documents. Document category judgment.
Text classification uses computers to automatically classify and mark text sets (or other entities or objects) according to a certain classification system or standard. It finds the relationship model between document features and document categories based on a collection of annotated training documents, and then uses this learned relationship model to judge the category of new documents. Text classification has gradually shifted from knowledge-based methods to methods based on statistics and machine learning.
Text classification generally includes the expression of text, the selection and training of classifiers, the evaluation and feedback of classification results, etc. The expression of text can be subdivided into text preprocessing, indexing and statistics, and feature extraction. Wait for steps. The overall functional modules of the text classification system are:
(1) Preprocessing: Format the original corpus into the same format to facilitate subsequent unified processing;
(2) Index: Decompose the document As a basic processing unit, it also reduces the cost of subsequent processing;
(3) Statistics: word frequency statistics, the correlation probability between items (words, concepts) and classification;
(4) Feature extraction: Extract features that reflect the topic of the document from the document;
(5) Classifier: training of the classifier;
(6) Evaluation: analysis of the test results of the classifier.
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