Natural Language Processing Techniques

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. It involves several techniques and methodologies that enable computers to understand, interpret, and generate human language. Here are some common NLP techniques:

• Tokenization: This process breaks down text into smaller units called tokens. For example, "Natural Language Processing" would be tokenized into ["Natural", "Language", "Processing"].
• Part-of-Speech Tagging: This technique identifies the parts of speech for each word in a sentence, such as nouns, verbs, adjectives, and adverbs.
• Named Entity Recognition (NER): NER identifies and classifies named entities in text, such as person names, organization names, locations, and more.
• Sentiment Analysis: This method determines the sentiment of a piece of text, such as whether it is positive, negative, or neutral.
• Machine Translation: This technique involves translating text from one language to another using automated tools.
• Text Classification: This method categorizes text into predefined categories based on its content.

NLP Techniques

To learn more about NLP, you can visit our Natural Language Processing Overview.