Part-of-speech (POS) tagging is the process of assigning each word in a text a specific part of speech, such as noun, verb, adjective, or adverb. POS tagging is an important step in natural language processing (NLP) because it enables a computer to analyze the grammatical structure of a text and extract meaning from it.

POS tagging is usually done using statistical models that learn from annotated data, which is a corpus of text that has been manually labeled with the correct part-of-speech tags. These models use various features, such as the context in which a word appears, its morphological properties, and its relationship to other words in the sentence, to predict the correct POS tag for each word.

POS tagging can be a challenging task because many words can have multiple possible POS tags, depending on the context in which they appear. For example, the word “bank” can be a noun meaning a financial institution, or a verb meaning to deposit money in a bank. In such cases, the POS tagger must rely on the surrounding words and other contextual information to make the correct prediction.

POS tagging is used in many NLP applications, such as information retrieval, sentiment analysis, machine translation, and text-to-speech synthesis, among others.