How does Czech NLP work?
- Tokenization. Tokenization involves breaking down text into individual words or phrases, making it easier for the NLP model to process and analyze the text.
- Stemming. Stemming reduces words to their base or root form, enabling the model to treat different forms of a word as a single entity.
- Named Entity Recognition (NER). NER identifies and classifies key entities in the text, such as names, dates, and locations, facilitating better understanding and context.
- Sentiment Analysis. Sentiment analysis determines the emotional tone of the text, allowing for the classification of content as positive, negative, or neutral.