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Document Classification #2: Supervised, Unsupervised and Semi-Supervised Classification

In part 2 of our 3-part article on Document classification we’ll delve into the several types of document classification. If you didn’t read the first part, you can check it out here!

How does document classification benefit your business?

Using AI, you have numerous benefits to better support your daily operations.

Saving time and resources

Automated document classification organizes and analyses large document collections, saving time and effort. It checks for errors, ensures completeness, and enables businesses to analyse unstructured data, identify patterns, and trends. This frees up employees for other tasks improving efficiency.

Automated decision making

Manual document classification can be confusing and time-consuming. Automatic document classification resolves this by providing control and facilitating faster decision-making.

For example, a company that handles numerous deliveries daily. With automatic document classification, you can categorize each order based on delivery date, contents, and more, ensuring a smooth process.

Improved customer satisfaction

Document classification improves customer satisfaction by automating customer service and resolving common issues efficiently.

By using document classification, the category of a customer issue can be quickly identified and directed to the relevant department. This eliminates the need for customers to wait for a representative and allows them to resolve their problems promptly.

Types of automatic document classification

There are multiple different approaches to automatic document classification, the most common are supervised, unsupervised and semi-supervised.

Supervised document classification

This method requires a training data set with labelled documents to accurately predict the category of new documents. It tries to find the relationship between the document and its category by looking at the labelled data.

As with any other method, there are some advantages and disadvantages.

  • Advantages – Easy to evaluate and more accurate than unsupervised methods.
  • Disadvantages – Requires a labelled training dataset. Can be time-consuming and expensive to label if the training dataset is large.

Unsupervised document classification

Na unsupervised approach doesn’t require a dataset to learn from. Instead, it attempts to classify documents by looking at the differences between them. The result is distinct groups containing similar documents; however, this approach doesn’t understand what those groups (categories) are. This approach is more difficult to evaluate.

  • Advantages – Faster and cheaper than a supervised approach. Doesn’t require a labelled training dataset.
  • Disadvantages – Less accurate than a supervised approach. More difficult to evaluate.

Semi-supervised document classification

This approach involves a mix between the previous two. Semi-supervised document classification uses both a labelled training dataset and unlabelled data, improving the performance of both supervised and unsupervised document classification.

  • Advantages – Can improve the accuracy of supervised and unsupervised approaches. Doesn’t require as much training data.
  • Disadvantages – More difficult to implement and less accurate.

TML: Texter Machine Learning | Supercharge your content with AI!

Your content and data are the foundation upon which your business operates, and critical decisions are made. Recent advancements in AI in areas such as image and natural language processing have enabled a whole new level of automatic extraction of information and data analysis that power the automation of key business processes not possible until now.

  • Process your data with different AI engines, integrating the results.
  • Supports several data formats: images, video, text, etc.​
  • Generate updated content and document versions based on AI results​
  • Store extracted information in metadata, enabling further processing and process automation.
  • On cloud or on-premises – in case you don’t want data to leave your private infrastructure
  • Compatible with several different ECM providers
  • Ability to develop custom AI models to target your specific needs and data

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