Which area of NLP involves classifying words according to their roles in a sentence?

The area of NLP that involves classifying words according to their roles in a sentence is part-of-speech tagging. This process assigns each word a label based on its grammatical category, such as noun, verb, adjective, adverb, etc. By understanding the structure of a sentence and the function that each word serves, part-of-speech tagging plays a crucial role in various NLP applications, including parsing, information extraction, and machine translation. It helps to establish the relationship between words and can enhance the interpretation of meaning within text.

In contrast, sentiment analysis focuses on determining the emotional tone behind a series of words, often identifying opinions or sentiments expressed in a text. Chatbot development involves creating conversational agents that can interact with users, drawing on various NLP techniques but not exclusively on classifying sentence roles. Text summarization entails condensing text into shorter versions while retaining the essential information, which does not primarily involve categorizing word roles in sentences.