Text Classification with NLPIR Deep Machine Learning

In recent years, with the rapid development of Internet, network information and data information are constantly expanding. How to effectively utilize this abundant data information has become one of the focuses of information technology workers. In order to quickly and accurately find out the information needed by users from a large amount of data information, automatic analysis of text information has also become an urgent need at present. One of the main techniques in the analysis of textual information is text classification.
Text classification is a basic problem in natural language processing, and many related studies can be attributed to classification problems. Text classification is the technique of classifying text into one or more categories according to certain rules. In recent years, many statistical methods and machine learning methods have been applied to text classification.
　　Text classification refers to the process of automatically determining the text category under a given classification system, according to the text semantic element is the atom in the statistical semantic method and the indivisible content. The minimum unit of the current text segmentation, the semantic element in the text classification is a word;
　　text classification generally includes the process of text expression, classifier selection and training, classification result evaluation and feedback, and text expression can be subdivided into text preprocessing, indexing and statistics, feature extraction and other steps. The overall functional modules of the text classification system are:
　　(1) Preprocessing: Format the original corpus into the same format, which is convenient for subsequent unified processing;
　　(2) Indexing: Decompose the document into basic processing units, while reducing the cost of subsequent processing;
　　(3) Statistics: word frequency statistics, related probability of items (words, concepts) and classification;
　　(4) Feature extraction: extract features from documents that reflect the subject of the document;
　　(5) Classifier: classifier training;
　　(6 ) ) Evaluation: Analysis of the test results of the classifier.
　　NLPIR adopts content-based automatic text classification filtering and rule-based text classification filtering classification, and uses deep neural network to comprehensively train the classification system. It can carry out multi-level classification, the classification speed is more than 100 articles per second, the average accuracy rate is more than 90%, and it can carry out Chinese and English classification and mixed classification of Chinese and English. Users can change templates flexibly and conveniently to achieve classification and filtering of different themes.
　　The text filtering function can quickly identify and filter out information that meets special requirements from a large amount of text, and can be used in brand report monitoring, spam blocking, sensitive information review and other fields.
　　NLPIR deep text classification can be used for news classification, resume classification, mail classification, office document classification, regional classification and many other aspects. In addition, text filtering can also be implemented, which can quickly identify and filter information that meets special requirements from a large amount of text, and can be applied to brand report monitoring, spam blocking, sensitive information review and other fields.
　　At present, the automatic classification of a large number of texts has become a hot spot in the fields of information retrieval, natural language processing, databases, artificial intelligence, etc.; text classification technology has become a key technology with great practical value, mainly reflected in the following fields: Information retrieval, automatic classification of Web documents, digital libraries, automatic summarization, classified newsgroups, text filtering, etc.