Data is everywhere—but it's no use if you don't know how to use it.
Suppose you’re an insurance provider and deal with a large amount of data daily. Part of your work involves separating this data into different insurance categories—such as health, financial, and accidental—so that the right teams can analyze, process, and act on them.
Manually classifying different insurance documents isn’t only time-consuming but also creates room for inconsistencies and errors.
So what’s the best solution forward? Automated document classification.
The global amount of data is expected to grow to more than 180 zettabytes in the next five years. Humans just aren't capable of coping with the ever-increasing data needs.
Using Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to analyze and classify documents automatically saves you time and effort—which you can better spend elsewhere.
In this guide, we’ll take a look at how document classification works, how it helps you save time, and how you can get started today.
Ready?
What is document classification?
Document classification is the process of assigning a document to relevant categories for easy management and analysis.
Automatic document classification techniques are paramount in information retrieval systems, such as search engines, for making it easier for users to find what they’re looking for. It’s faster, more efficient, and more convenient than manual classification—saving significant time and money.
With document classification, the goal is to create a classification model that can accurately assign documents to the right categories.
Document vs. text vs. image classification
A document may contain text and images; document classification can be textual and visual based on these elements. Let’s take a look:
Text classification
Text classification is a sub-task of document classification and deals specifically with text. This can be a sentence, a paragraph, or even an entire document (text-only).
Additionally, text classification is generally more complex than document classification because there is often less context to work with. With document classification, the entire document can be used as context, while with text classification, only the text itself is available.
Text classification uses Natural Language Processing to understand text-based content, such as words and phrases, and group them based on context using text classifiers.
Image classification
Image classification is a subset of document classification and deals with only images instead of whole documents.
Visual classification categorizes images and other visual documents using Computer Vision, object recognition, and image recognition technology based on visual behavior and attributes.
The AI model can then categorize these images depending on a variety of different criteria.
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.
Supervised document classification
Supervised methods require a training data set with labeled documents to predict the category of new documents accurately. Conceptually, supervised methods attempt to find a relationship between the document and its category by looking at labeled historic data.
Advantages of supervised document classification:
- It can be more accurate than unsupervised methods.
- Easy to evaluate.
Disadvantages of supervised document classification:
- Requires a labeled training dataset.
- It can be time-consuming and expensive to label a large training dataset.
Unsupervised document classification
Unlike supervised document classification, unsupervised methods don’t require a data set to learn from and instead attempt to classify documents by looking just at the differences between documents. This yields different clusters which contain similar documents, however, this method doesn’t understand what those clusters (i.e. categories) are. This can be done using methods such as clustering and topic modeling.
Unsupervised methods are more difficult to evaluate than supervised methods but can be very powerful when used correctly.
Advantages of unsupervised document classification:
- It doesn't require a labeled training dataset.
- It can be faster and cheaper than supervised methods since no labeling is required.
Disadvantages of unsupervised document classification:
- It is more difficult to evaluate.
- It can be less accurate than supervised methods.
Semi-supervised document classification
Semi-supervised involves a mix of supervised and unsupervised methods. The semi-supervised method uses both a labeled training set and unlabeled data and can improve the performance of supervised and unsupervised document classification.
Advantages of semi-supervised document classification:
- It can improve the accuracy of supervised and unsupervised methods.
- It doesn't require as much training data as a supervised method.
Disadvantages of semi-supervised document classification:
- It is more difficult to implement than either supervised or unsupervised methods.
- It can be less accurate than completely supervised method.
How does document classification work?
Document classification categorizes documents into different categories. This can be done manually or automatically.
When done manually, a person reviews the document and assigns it to a category, which can be time-consuming and error-prone.
When done automatically, Deep Learning algorithms classify documents into different categories without human guidance.
Here’s how it works, step by step:
- Gather a dataset: First, collect a dataset. It should be large enough (we recommend at least 20 data points per label) to train a classification model, a subset of machine learning algorithms that categorize outputs based on specific inputs and represent the data you want to classify.
- Train the model: Once decided on a dataset, you need to train the model. This process can be time-consuming depending on the chosen tool or method, but is necessary to get accurate results. Training can be supervised, unsupervised, or semi-supervised.
- Evaluate results: Benchmarking results against expectations is essential to ensure your model performs as intended. You can do this by automatically assigning a predicted document to a team member responsible for measuring the accuracy of your predictions.
Overall, getting started with document classification isn’t difficult. However, it’s important to take the time to understand the process. This ensures you get the best results from your document classification efforts.
Why is document classification beneficial for business?
You can leverage document classification in many ways to support your daily operations and bottom line—whether it’s AI for small business or large-scale machine learning efforts.
AI saves time and resources
Document classification automatically organizes and analyzes large collections of documents. This can save time and effort that would otherwise be spent manually organizing documents.
Document classification can also checks documents for completeness or errors and helps businesses analyze unstructured data, identify patterns and trends.
Automated classification frees employees for other tasks—such as helping customers with complex issues—and improves overall efficiency.
AI helps you automate decision making
Manually classifying documents can confuse you about what to categorize and how. Automatic document classification solves this pain point by giving you more control over how you classify documents, enabling better and faster decision-making.
Let's say you're a shipping company that handles tons of deliveries every day. Some shipping requests may be expedited shipments, while others may be regular shipments. Automatic document categorization enables you to quickly categorize each order by delivery date, contents, and more to ensure the process is as smooth as possible.
All you have to do is create a list of labels and add them to your document classification system. The system will automatically analyze and categorize each shipping type, saving you the manual effort of deciding what to prioritize or treat with care.
Improve customer satisfaction
Document classification can improve customer satisfaction by automating customer service and resolving mundane issues.
With document classification, you can quickly and easily identify the category of a customer issue and route it to the appropriate department. This means customers can resolve their issues faster without waiting for a customer service representative.
Document classification applications and use cases
Document classification can be used for various tasks such as sentiment analysis, topic modeling, and spam detection. Here are some of the ways businesses use document classification in day-to-day operations.
Content moderation
Content moderation involves identifying and removing offensive or inappropriate content. Document classification can be used to moderate the content automatically.
You can train machine learning models to classify documents into different categories, such as hate speech, profanity, NSFW, and more. Content classification can then be removed or flagged for review.
Content moderation with Levity is quick and easy to set up, take a look:
Simply integrate the platform with your other tools, pull the data tor un through your AI model, and start classifying documents into your predetermined categories.
Classify customer support tickets
Another common application is customer support ticket classification. Document classification can be used to classify customer support tickets into different categories. This can help route tickets to the appropriate team or department so customer service representatives can resolve issues faster.
To get started, you need a dataset of customer support tickets. Once you have a dataset, you can train a model and start classifying your tickets.
The AI model analyzes each incoming support request and evaluates it based on the criteria you’ve set, using the data set you provided. This information enables you to provide more streamlined support to customers and saves your customer support team valuable time.
Levity’s AI flows enable you to automate next steps, too:
Classifying support tickets is simple with Levity's powerful Machine Learning capabilities.
Check documents for completeness
Document classification can also be used to check documents for completeness. This ensures that incoming documents include all the necessary information to move things along. This could include ensuring all fields are complete, or ensuring there’s a valid signature.
Checking documents for completeness limits the administrative hassle associated with getting client input and avoids the back and forth timewasting.
The AI model assesses the document, verifies if all input is complete, and forwards it to your inbox. If the input is incomplete, you can assign a different output action—maybe an automated reminder to clients that the document is incomplete.
Check onboarding documents
Checking onboarding documents for errors or incompleteness is an essential task—-but it can also end up being very time-consuming and error-prone. Onboarding is when customers get their first real look inside your product—you want to ensure you make a great first impression.
Document classification can help speed up the onboarding process by quickly identifying documents and their completeness. For example, below is an AI model built to classify documents by type: articles of inc., NDA, bank statement, and terms of use.
The model doesn’t stop there, though—it then classifies certain documents again, as we can see with NDAs being classified as signed or unsigned.
Having an AI model to do this work for you speeds up the onboarding process and offers a streamlined onboarding experience for new users.
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Tag email attachments
Tagging email attachments can be troublesome and tedious. Document classification categorizes emails by attachment type, such as PDFs, images, or spreadsheets. It further routes emails with attachments to the appropriate team or department.
This ensures documents get to the right department the first time round. Finance doesn’t want to be sending HR contracts about as much as HR doesn’t want to spend their time forwarding invoices to finance.
With AI-powered email categorization, you can ensure your team is able to focus on their tasks, and their tasks alone.
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Classify shipping documents
Another application of document classification is in the shipping industry. Classifying shipping documents into different categories helps keep shipping in check and ensures all packages include the correct information—such as an invoice, packing list, certificate of origin, and more.
Manually handling shipping documents—such as checking ERP information or adding tags in the TMS to release the shipment—may require developers or a project team. But with an automated document classifier, you can recognize any shipping document in order to categorize it correctly.
AI models auto-handle shipping documents from receipt to storage. When you automate mundane shipping tasks, employees have more time and energy to cater to other, more strategic areas, employee dissatisfaction goes down, focus on high-value areas increases, and you gain enhanced insights from the improved classification process.
How to get started with document classification with Levity
Getting started with document classification is easy with Levity—let’s take a look at the process from start to finish.
Gather data
Collecting quality data is the first step in building a document classification system. You can retrieve data in a number of ways.
You can gather data from your operations in order to create a dataset for training your AI model, or you can download a data set from a third-party site. For example, Kaggle gives users access to a repository of community-published data.
If you're having trouble getting the right data, you can start with Levity without a predefined data set and build your own database for your use case along the way. Edit: with a minimum of X data
Upload data
The next step involves uploading the data to your dashboard. You can either upload labeled data—which gives each document a category.
Create and train a custom AI model
Build and train a custom AI model with Levity's intelligent no-code automation engine. The trained model can automatically analyze and classify data without specific inputs, reducing manual work and saving time.
You can also automatically validate your AI model and check for inaccuracies. Levity displays errors by type and notifies you whenever the prediction’s accuracy is below the threshold you have previously set.
Connect your AI block to apps
Once you've checked your AI block, you're all set with the classification system.
However, it doesn't stop there. Levity offers multiple integrations and allows you to connect your AI block to different apps like Zapier, Make, and Bubble.io to enable the next steps following the AI model’s classification.
Start classifying documents according to your specific needs
Many documents don’t just contain text—you need a document classification system that allows you to work with different formats and types of documents.
Many small businesses don't have the resources to invest in data scientists and development teams. They need a no-code solution that saves them hassle and money and helps classify documents according to their contents.
Levity covers all bases when it comes to personalized document classification. Its automated, no-code AI solution gives you a wealth of options without breaking the bank. You get best-in-class AI capabilities tailored to your specific classification needs.
Make your life easier with Levity's suite of document classification features.
