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Four data classifications are used by the university: Controlled Unclassified Information, Restricted, Controlled and Public. The Data Trustee is ultimately responsible for deciding how to classify their data (see Roles and Responsibilities for list of Data Trustees and additional information).

Types of automatic document classification. Machine Learning engineers approach automatic document classification in many ways; the three most common are supervised, unsupervised, and semi-supervised.

Document classification refers to a process of assigning one or more labels for a document from a predefined set of labels. The main issues in document classification are connected to classification of free text giving document content.

Some Examples of Text Classification: Sentiment Analysis. Language Detection. Fraud Profanity & Online Abuse Detection. Detecting Trends in Customer Feedback. Urgency Detection in Customer Support.

Classification can be divided to two categories: supervised classification or with model and unsupervised classification or without model. In supervised classification, labeled documents are group into pre-determined classes.

Rule-based approaches classify text into organized groups by using a set of handcrafted linguistic rules. These rules instruct the system to use semantically relevant elements of a text to identify relevant categories based on its content. Each rule consists of an antecedent or pattern and a predicted category.

When it comes to separating the useful information from the irrelevant, document classification is a worthwhile tool that can reduce the cost and time of searching and retrieving the information that matters.

Concept Mining. Rough set-based classifier. Soft set-based classifier.

Classified information is material that a government body deems to be sensitive information that must be protected. Access is restricted by law or regulation to particular groups of people with the necessary security clearance and need to know, and mishandling of the material can incur criminal penalties.

Document classification is an example of Machine Learning (ML) in the form of Natural Language Processing (NLP). By classifying text, we are aiming to assign one or more classes or categories to a document, making it easier to manage and sort.