Natural Language Processing (Nlp)

Natural Language Processing (NLP) enables machines to understand and generate human language, playing a crucial role in applications like virtual assistants and sentiment analyzers.

Introduction To Natural Language Processing

NLP is a subfield of AI that enables machines to understand and generate human language, applicable in various technologies like virtual assistants.

Text Processing And Tokenization

Text Processing and Tokenization are fundamental steps in Natural Language Processing (NLP) that prepare and convert raw text into structured data for machine analysis.

Text Processing

Text processing is a critical preliminary step in NLP that involves cleaning and structuring raw text data.

Tokenization

Tokenization is the process of breaking text into smaller units called tokens, which are typically words or sentences, enabling easier analysis by NLP systems.

Language Models And Part-Of-Speech (Pos) Tagging

This section covers language models and the significance of part-of-speech tagging in natural language processing, emphasizing their roles in understanding and generating human language.

Language Models

Language models are essential tools in NLP, used to predict the probability of sequences of words.

Part-Of-Speech (Pos) Tagging

Part-of-Speech (POS) tagging assigns grammatical categories to each word in a sentence, assisting in understanding sentence structure.

Sentiment Analysis And Chatbots

This section covers sentiment analysis and chatbots within the Natural Language Processing (NLP) domain, detailing their functionalities and applications.

Sentiment Analysis

Sentiment analysis identifies the emotional tone of text, categorizing it as positive, negative, or neutral.

Chatbots

Chatbots are AI-driven conversational agents that interact with users through natural language, leveraging techniques like intent and entity recognition.