Token Classification
Token classification is a natural language processing (NLP) task that involves assigning labels to individual tokens (words or subwords) in a text sequence. It is used to identify and categorize specific elements such as named entities, parts of speech, or other linguistic features. Common applications include named entity recognition (NER), part-of-speech tagging, and chunking.
Developers should learn token classification when working on NLP projects that require fine-grained text analysis, such as information extraction, sentiment analysis, or language understanding. It is essential for tasks like identifying people, organizations, and locations in documents, or preprocessing text for downstream machine learning models. Mastery of token classification is crucial for building robust NLP systems in fields like healthcare, finance, and customer service.