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Natural language processing (NLP) is a form of artificial intelligence (AI) that allows computers to understand human language, whether you write it, speak it, or even scribble it. As AI-powered devices and services become increasingly more intertwined with your daily life and the world, so too does the impact that NLP has on ensuring a seamless human-computer experience.

In this article, you'll learn more about what NLP is, the techniques used to create it, and some of the benefits it provides consumers and businesses.听Afterward, if you'd like to master cutting-edge NLP techniques yourself, consider enrolling in DeepLearning.AI's Natural Language Processing Specialization.

Natural language processing definition

Natural language processing (NLP) is a subset of artificial intelligence, computer science, and linguistics focused on making human communication, such as speech and text, comprehensible to computers.听

NLP is used in a wide variety of everyday products and services. Some of the most common technologies that use NLP are voice-activated digital assistants on smartphones, email-scanning programs used to identify spam, and translation apps that decipher foreign languages.

What are NLP techniques?听

NLP encompasses a wide range of techniques to analyze human language. Some of the most common techniques you will likely encounter in the field include:

• Sentiment analysis: An NLP technique that analyzes text to identify its sentiments, such as 鈥減ositive,鈥� 鈥渘egative,鈥� or 鈥渘eutral.鈥� Sentiment analysis is commonly used by businesses to better understand customer feedback.听

• Summarization: An NLP technique that summarizes a longer text in order to make it more manageable for time-sensitive readers. Some common texts this technology can summarize include reports and articles.听

• Keyword extraction: An NLP technique that analyzes a text to identify the most important keywords or phrases. Keyword extraction is commonly used for search engine optimization (SEO), social media monitoring, and business intelligence 辫耻谤辫辞蝉别蝉.听

• Tokenization: The process of breaking characters, words, or subwords down into 鈥渢okens鈥� that the program can analyze. Tokenization undergirds common NLP tasks like word modeling, vocabulary building, and frequent word occurrence.

NLP benefits听

Whether it鈥檚 being used to quickly translate a text from one language to another or producing business insights by running sentiment analysis on hundreds of reviews, NLP provides both businesses and consumers with a variety of benefits.听

Unsurprisingly, then, you can expect to see more of it in the coming years. According to research by Fortune Business Insights, they project the global market for NLP to grow from $29.71 billion in 2024 to $158.04 billion in 2032 [].听

Some common benefits of NLP include:

• The ability to analyze both structured and unstructured data, such as speech, text messages, and social media posts.听

• Improving customer satisfaction and experience by identifying insights using sentiment analysis.听

• Reducing costs by employing NLP-enabled AI to perform specific tasks, such as chatting with customers via chatbots or analyzing large amounts of text data.听

• Better understanding a target market or brand by conducting NLP analysis on relevant data like social media posts, focus group surveys, and reviews.听

NLP limitations

NLP can be used for a wide variety of applications, but it's far from perfect. In fact, many NLP tools struggle to interpret sarcasm, emotion, slang, context, errors, and other types of ambiguous statements. This means that NLP is mostly limited to unambiguous situations that don't require a significant amount of interpretation.

Natural language processing examples

Although natural language processing might sound like something out of a science fiction novel, the truth is that NLP examples already exist in your everyday life as you interact with countless NLP-powered devices and services every day.听

Online chatbots, for example, use NLP to engage with consumers and direct them toward appropriate resources or products. While chatbots can鈥檛 answer every question that customers may have, businesses like them because they offer cost-effective ways to troubleshoot common problems or questions that consumers have about their products.听

Another common use of NLP is for text prediction and autocorrect, which you鈥檝e likely encountered many times before while messaging a friend or drafting a document. This technology allows texters and writers alike to speed up their writing process and correct common typos.听

What about ChatGPT?

ChatGPT鈥攁 chatbot powered by AI and natural language processing鈥攑roduces unusually human-like responses. Recently, it has dominated headlines due to its ability to produce responses that far outperform what was previously commercially possible.

If you'd like to learn more, the University of Michigan's ChatGPT Teach Out brings together experts on communication technology, the economy, artificial intelligence, natural language processing, health care delivery, and law to discuss the impacts of the technology now and into the future.

Natural language processing applications

NLP has a wide array of applications across various sectors, such as finance, insurance, and health care. Prominent applications for NLP technology include voice-activated assistants, machine translation, sentiment analysis, chatbots, virtual customer support, classification and categorization, content recommendation systems, text summarization, speech recognition, and natural language generation.听 Here are just some of the ways natural language processing is used in the real world:

1. Voice-activated assistants

NLP powers virtual assistants, so if you ever use Apple鈥檚 Siri, Amazon鈥檚 Alexa, and IBM鈥檚 watsonx Assistant, you鈥檝e already experienced NLP. This technology enables them to understand and respond to voice commands. It allows you to interact with your device using natural language to perform tasks, search for information, and control smart home devices.

2. Machine translation

NLP is the driving force behind machine translation services such as Google Translate. It allows for the automatic translation of text and speech between languages, making global communication more accessible. NLP allows an online translator to understand the individual rules of grammar and language structure between two languages and effectively decode one into the other.

3. Sentiment analysis

Your business can use NLP for sentiment analysis to gauge a customer鈥檚 opinion, their satisfaction, and the market鈥檚 response to your products by analyzing social media posts, customer reviews, and survey responses. This can help your company make better decisions, especially when formulating future strategies.

4. Chatbots and virtual customer support

NLP enables chatbots to understand and respond to customers' questions and comments in a conversational manner. This application is widely used in customer service to provide instant assistance, book appointments, and resolve common issues.

5. Content recommendation systems

Platforms like T-Mobile, Spotify, and Disney+ use NLP-based recommendation systems to analyze user preferences and provide personalized content suggestions based on previous interactions and the content's textual data. Using sentiment analysis鈥攁lso powered by natural language processing鈥攔ecommendation systems can even recommend movies, music, or other media based on how users have reviewed those products.听

6. Speech recognition

Speech recognition assists with converting spoken language into text in real-time, which is essential for dictation software, hands-free computing, and real-time transcription services. Everybody talks a little differently: At different speeds, in varying tones, with accents and regional dialects, and with differing pronunciations. After the speech recognition software transcribes your words, natural language processing analyzes those words, determines the meaning behind them, and then formulates an appropriate response.

7. Natural language generation (NLG)

This involves using NLP to generate natural language text from data, enabling applications like automated report generation, personalized content creation, and article writing. NLG can also craft stories. With natural language generation, you can ask an AI language model like Amazon鈥檚 Alexa or Apple鈥檚 Siri a question as if speaking to another person. The model will respond similarly.听

These applications demonstrate the versatility and impact of NLP in simplifying interactions, enhancing accessibility, and providing deeper insights from textual data across diverse domains.

Natural language processing tools

You can choose from the numerous natural language processing tools and services available to help you start working with NLP today. Some of the most common tools and services you might encounter include the following:听

• Google Cloud NLP API

• IBM Watson听

• Amazon Comprehend

Natural language processing with Python听

Python is a programming language well-suited to NLP. Some common Python libraries and toolkits you can use to start exploring NLP include NLTK, Stanford CoreNLP, and Genism.听

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Natural language processing helps computers understand human language in all its forms, from handwritten notes to typed snippets of text and spoken instructions. Start exploring the field in greater depth by taking a cost-effective, flexible Specialization on 糖心vlog官网观看.听

顿别别辫尝别补谤苍颈苍驳.础滨鈥檚 Natural Language Processing Specialization can help you prepare to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and build chatbots.

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