NLP aims to bridge the gap between human language and computer systems, allowing computers to comprehend and respond to natural language input and generate human-like text.

Sentiment Analysis involves determining the emotional tone or opinion expressed in a piece of text, which is a core task in NLP.

Parsing is the process of analyzing the grammatical structure of a sentence, identifying its components such as nouns, verbs, and phrases.

Stemming is a process that reduces words to their root form, removing suffixes and prefixes to obtain the core meaning of the word.

Part-of-Speech Tagging involves assigning grammatical tags (e.g., noun, verb, adjective) to each word in a sentence.

Named Entity Recognition aims to identify and classify specific entities in a text, such as names of people, organizations, locations, and dates.

Summarization involves creating a condensed version of a text that captures its main points and key information.

Machine Translation involves using statistical or neural network models to translate text from one language to another.

Question Answering involves developing systems that can automatically answer questions based on the information extracted from a given text.

Natural Language Generation involves using statistical or neural network models to generate human-like text.

Information Extraction involves extracting structured data from unstructured text, such as names, dates, and locations.

Sentiment Analysis involves determining the emotional tone or opinion expressed in a piece of text.

Language Identification involves determining the language in which a given text is written.

Spell Checking involves identifying and correcting spelling errors in text.

Text Vectorization involves converting text into a numerical representation, such as a vector of word frequencies or a matrix of word embeddings.