How does Nepali NLP work?
- Tokenization. Tokenization is the process of breaking down text into smaller units, called tokens, which may be words, phrases, or sentences. This step is crucial for enabling further processing and analysis of the text.
- Part-of-Speech Tagging. Part-of-Speech Tagging involves assigning parts of speech to each token in the text, such as noun, verb, adjective, etc. This helps in understanding the grammatical structure and meaning of the sentences.
- Named Entity Recognition. Named Entity Recognition (NER) identifies and classifies key elements in the text into predefined categories like names of people, organizations, and locations, facilitating enhanced comprehension and data extraction.
- Sentiment Analysis. Sentiment Analysis is the technique of determining the sentiment behind a piece of text, whether it is positive, negative, or neutral, which is valuable for understanding public opinion and emotional tone in communication.
