Natural language processing (NLP)
Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language using computational linguistics, machine learning, and...
NLP enables computers to understand and process human language, powering applications like chatbots, translation, and sentiment analysis.
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. The goal of NLP is to enable computers to understand, interpret, and generate human language in a way that is meaningful and useful. NLP combines computational linguistics—rule-based modeling of human language—with machine learning, statistical, and deep learning models.
NLP operates through a series of stages to transform raw text into meaningful data that machines can understand and act upon. Here are the main phases:
This initial phase involves cleaning and preparing the text data for analysis. Techniques include tokenization, stemming, lemmatization, and stopword removal.
This phase involves the application of various machine learning and deep learning algorithms to model the text data. The algorithms can be rule-based, statistical, or neural network-based, depending on the complexity of the task.
NLP has a wide range of applications across various industries. Here are some notable examples:
Natural Language Processing (NLP) is a branch of AI focused on enabling computers to understand, interpret, and generate human language, combining computational linguistics with machine learning and deep learning.
NLP is used in chatbots, virtual assistants, text translation, sentiment analysis, voice recognition, and content summarization across various industries.
NLP works through stages like data preprocessing, algorithm development using machine learning or deep learning, and semantic analysis to process and derive meaning from human language.
Key techniques include tokenization, stemming, lemmatization, POS tagging, dependency parsing, named entity recognition, sentiment analysis, and machine translation.
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