Document Classification

Text Classification Methods

Rule-Based System

In this approach, a set of linguistic rules are applied to separate the texts and classify them in an organized group.

Machine System

This approach allows making classifications based on previously trained model. The pre-labeling of user data is done after training the model on previous dataset.

Hybrid System

This approach combines both machine-based and rule-based approaches. It uses the rule-based system approach to create data tags and rules for machine learning models. The hybrid system is considered the best method for document classification.


How does UBIAI’s Text Classification Feature help in Machine Learning Models?

The process of categorizing text into a group of words is referred to as text classification. UBIAI’s text annotation tool helps in developing NLP and train machine learning models. 


While developing NLP, the text classification feature of UBIAI helps in automatically analyze text and assigns it to a predefined set of tags or categories. 


Everything is done based on the relevant context and accurate classification. With the help of text classification, NLP can be used for topic detection, language detection, and sentiment analysis


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If you’re looking for a tagging platform on NLP projects, UBIAI probably has all the features you need. lt supports many document types and multiple OCR engines. It’s super simple and powerful.

Ihsan S.

Machine Learning Engineer

It is very easy to work with. A couple of things that draw me to UBIAI are that it could accept data in a LOT of formats (even including PDFs!).

Jetson W.

Data Architect

I love this product as this provides amazing support for document classification and named entity recognition. The NLP tasks UI is very intuitively embedded into this product and it is really easy to use.

Jasmeet K.

Data Scientist