Major Subfields of AI - Natural Language Processing (NLP)

 Natural Language Processing (NLP) is a subdiscipline of AI that aims at allowing computers to comprehend and manipulate human language. The most important ones are Natural Language Understanding (NLU) and Natural Language Generation (NLG), which processes and generates language respectively. The fundamental operations include text analysis by use of tasks such as tokenizing and part-of-speech tagging and the applications are broadly used in areas such as translation, sentiment analysis and chatbots.

Core componentsNatural Language Understanding (NLU): The process of enabling a computer to derive meaning from human language, often involving mapping input to a useful representation.
Natural Language Generation (NLG): The process of producing meaningful human-like text from data or an internal representation.

Common tasks and techniques

• Text Preprocessing:Tokenization: Breaking text into smaller units like words or sentences.
• Part-of-Speech (POS) Tagging: Assigning a grammatical category (e.g., noun, verb) to each word.
• Named Entity Recognition (NER): Identifying and categorizing entities such as people, organizations, and locations.
• Syntax and Parsing: Analyzing the grammatical structure of a sentence.
• Semantic Analysis: Understanding the meaning of words in a given context.
• Discourse and Pragmatic Analysis: Analyzing the relationships between sentences and understanding context.

Key applications

• Machine Translation: Translating text from one language to another.
• Sentiment Analysis: Determining the emotional tone or opinion expressed in text, often used for social media and customer feedback.
• Chatbots: Creating conversational interfaces that can understand and respond to user input.
• Text Summarization: Automatically creating a concise summary of a larger document.
• Information Extraction: Pulling specific, useful data from unstructured text.