Syntactic analysis, or parsing, focuses on the arrangement and relationship of words in a sentence. It helps decipher the grammatical structure of sentences, identifying subject-verb-object relationships and other syntactic constructs. Semantics: Semantic analysis goes beyond syntactic structure to understand the meaning of words and sentences. It involves extracting the intended meaning of words and discerning the contextual nuances that contribute to overall comprehension. Named Entity Recognition (NER): NER involves identifying and classifying named entities within a text, such as names of people, locations, organizations, and other specific entities. This is crucial for extracting valuable information from unstructured data. Sentiment Analysis: Sentiment analysis, also known as opinion mining, assesses the emotional tone expressed in a piece of text. It classifies the sentiment as positive, negative, or neutral, enabling systems to gauge public opinion or user sentiment.

Machine Translation: Machine translation aims to automatically translate text from one language to another. It employs sophisticated algorithms and models to understand the meaning of sentences in the source language and generate accurate translations in the target language. NLP is fundamental to various applications, including chatbots, virtual assistants, language translation services, sentiment analysis tools, and more. As technology advances, NLP continues to play a pivotal role in enhancing human-computer interactions and making information more accessible and actionable.