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