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What is Natural Language Processing?Natural Language Processing in Real LifeProblems that NLP can SolveExtra ResourcesBooksVideos

Last Updated: 2/6/2023

Natural Language Processing - NLP

What is Natural Language Processing?

Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. It involves developing algorithms and models that enable computers to process, understand, and generate human language meaningfully. This can include sentiment analysis, machine translation, text classification, and more. NLP uses statistical and machine learning models to analyze and understand the patterns and structures in human languages. These models are trained on large datasets of text, allowing them to make predictions and generate responses based on that data.

NLP models have been exposed to a large amount of text and learned to identify the patterns and relationships between words and phrases. Think about it like a "Rosetta Stone" for computers, allowing them to translate human languages into a form they can understand and work with, just like the Rosetta Stone helped scholars decipher ancient Egyptian hieroglyphs.

Natural Language Processing in Real Life

Natural Language Processing (NLP) is being used in a wide range of applications, including:

  • Search engines: to understand and interpret the intent behind search queries, which allows search engines to return more relevant results.
  • Virtual assistants: NLP enables virtual assistants like Siri, Alexa, and Google Assistant to understand and respond to natural language queries.
  • Chatbots: to understand and respond to natural language queries used in customer service, e-commerce, and other areas.
  • Social media monitoring: analyzing and extracting insights from social media data, such as determining the sentiment of tweets or understanding customer feedback.
  • Text classification: NLP algorithms can automatically classify text into different categories, such as spam detection, news categorization, and sentiment analysis.
  • Language translation: to translate text from one language to another, which have been used in a wide range of applications, such as website localization, document translation, and chatbot localization
  • Text-to-speech and Speech-to-text: NLP is very popular in cognitive services, generating speech from text and transcribing speech-to-text. These techniques are widely used in personal assistants, call centers, and other applications.
  • Named entity recognition: NLP extracts information from unstructured text, such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
  • Summarization: NLP is used to summarize long documents, articles, and news into shorter versions, used in news aggregators and information retrieval systems.

Problems that NLP can Solve

  • Sentiment Analysis. Sentiment analysis determines the sentiment or emotion expressed in a piece of text, such as a social media post or product review.
  • Text Classification. Text classification categorizes a text document into several predefined categories based on its content.
  • Named Entity Recognition. Named entity recognition identifies and classifies named entities in a text, such as people, organizations, locations, and dates.
  • Part-of-Speech Tagging. Part-of-speech tagging marks each word in a text with its corresponding grammatical category, such as noun, verb, adjective, and so on.
  • Text Summarization. Text summarization reduces a long text to a shorter version that retains its main ideas and content.
  • Machine Translation. Machine Translation automatically translates a text from one language to another.
  • Text Generation. Text generation automatically generates text based on input data, such as a prompt or a set of rules.
  • Chatbots and Conversational Agents. Chatbots and conversational agents are computer programs that can engage in natural language conversations with humans, such as virtual assistants and customer service chatbots.
  • Text Categorization. Text categorization is assigning a text document to one or more predefined categories based on its content.
  • Question Answering. Question answering automatically answers questions in natural languages, such as retrieving information from a database or document collection.

These are some of the most common applications of NLP, but the field is constantly evolving and there are many more emerging applications and areas of research.

Extra Resources

Books

This book provides a comprehensive introduction to the field of NLP, covering the fundamentals of speech and language processing, computational linguistics, and speech recognition. It is suitable for both beginners and intermediates.

This book provides a hands-on guide to NLP in Python using the Natural Language Toolkit (NLTK) library. It covers the basics of NLP, such as text processing, tokenization, and sentiment analysis, and is suitable for both beginners and intermediates.

This book is an excellent resource for beginners to learn about NLP as it provides a hands-on, practical approach to understanding and implementing NLP systems. The book is designed to provide practical, actionable information on how to build NLP systems. It covers various NLP topics, including text processing, feature engineering, text classification, and more. The writing style is clear, concise, and accessible, making it easy for beginners to understand even complex concepts. And the book focuses on real-world NLP applications and provides practical tips and guidance on how to solve common NLP problems.

Videos