How does Kannada NLP work?
- Tokenization. Tokenization involves splitting text into individual words or phrases, making it easier for further processing in NLP tasks.
- Sentiment Analysis. Sentiment analysis assesses the sentiment or emotional tone behind a series of words, essential for understanding public opinion in Kannada texts.
- Named Entity Recognition. This technique identifies and classifies key entities in text (like names, dates, and locations) to enhance information retrieval in Kannada documents.
- Machine Translation. Machine translation converts text from Kannada to other languages and vice versa, facilitating communication and content accessibility.
