A. Part-of-Speech (POS) tagging is a preprocessing step in natural language processing (NLP) that involves assigning a grammatical category or part-of-speech label (such as noun, verb, adjective, etc.) to each word in a sentence. It serves several purposes as a preprocessing step: 1. Syntactic Analysis: POS tagging helps in understanding the grammatical structure of a sentence. It provides information about the roles of words in forming phrases and sentences, aiding in syntactic analysis. 2. Feature Extraction: POS tags can be useful as features for various NLP tasks, such as text classification, named entity recognition, and machine translation. Different POS tags often convey different semantic or contextual information about words. 3. Disambiguation: Many words in natural language have multiple possible interpretations (polysemy). POS tagging helps disambiguate word senses based on their grammatical context. 4. Language Modeling: POS tagging is often used as a building block for language models, providing information about the relationships between words and their likely syntactic roles. 5. Rule-Based Processing: POS tags can be used in rule-based processing to identify patterns and grammatical structures in text. 6. Lemmatization and Stemming: POS information is valuable for lemmatization and stemming, where words are reduced to their base forms. 7. Parsing and Grammar Checking: POS tagging aids in syntactic parsing and grammar checking by providing information about how words function within sentences. In summary, POS tagging is a fundamental preprocessing step that helps enhance the accuracy and effectiveness of various NLP tasks by providing insights into the grammatical and syntactic structure of textual data.