Classification of texts or documents is the task of computational linguistics, which consists of assigning a document to one of several categories based on the content of the document. Programs that categorize documents automatically help keep your documents in order and not lose anything important. In this post, we are going to look at how you can use machine learning solutions to help you classify documents in your organization.

What Tasks Does ML Document Classification Simplify?

The classification of texts can be used in different domains, for example:

  • Dividing documents, books, web pages into thematic catalogs;
  • Spam filtering;
  • Defining the language of the text;
  • Creating personalized more relevant ads.

Machine learning algorithms can help you to deal with all of these tasks. I’ve seen a good explanation of different types of classification algorithms here.

How to Use ML for Text Classification?

Classification of documents with the help of ML can be either supervised or unsupervised. 

In supervised learning, the programmer helps the machine to learn by providing labeled examples and setting up rules. The machine sometimes needs to be corrected in its guesses and it is the human specialist who does that.

In unsupervised learning, the machine is presented with lots of data and is supposed to figure out the rules on its own. 

However, the process of creating a document classification system is very similar to the process of creating other systems using machine learning:

It is necessary to collect the documents for training the classifier. Like humans, the machine most effectively learns from examples. The more examples, the better. Usually, we are talking about thousands of samples. However, some algorithms are able to work well with relatively small samples, for example, Naive Bayes.

Each document from the training collection must be represented as a feature vector. The computer is not able to work with unstructured data. You need to prepare it using one of the database creation tools so that your algorithm could process it. 

For each document you need to indicate the "correct answer", ie. label the expected result. Using these answers, the classifier will be trained. In supervised learning, the programmer acts as a ‘teacher’ who assesses the model’s performance and sees whether it manages to classify samples correctly. The more time you dedicate to this process the better your model will be. In unsupervised learning, this step is omitted but more attention is paid to testing and validation.

Choose a classification algorithm and train the classifier. Now it is time to write your algorithm. Usually, you don’t have to write everything from scratch and can use one of many ML libraries available for all the most popular programming languages such as Python, Java, C++. 

Test the model. The accuracy of the model must be tested on new samples that the machine has not yet seen. This helps to fight overfitting. It might take you a few iterations of testing and fine-tuning the model before you get the optimal performance.

Use the resulting model for real-life tasks. Finally, when your model is ready you can implement it in your organization to classify documents. 

Building a document classification model with the help of ML is quite simple but it helps you to improve the company’s performance a lot. 

 

Author's Bio: 

I am an author at selfgrowth