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NLP is an area of AIthat deals with the interaction between computers and human (natural) language. NLP enables machines to understand, interpret, and generate human language. A prominent example of NLP in action is chatbotsnatural language by Voice assistants like Alexa and Siri. These technologies use NLP to comprehend spoken commands and generate natural language responses that match user requests. For instance, a user can ask: "Will it rain tomorrow?" and the assistant can analyze the weather forecast, respond in natural language, and hopefully not turn vacation plans upside down.
NLU is a subfield of Natural Language Processing that specializes in grasping the meaning and intent behind words. While NLP covers the broad process of understanding and generating language, NLU focuses on precisely what was said and what the speaker means by it. An example of this is interactive voice assistants that can understand complex commands and act upon them, such as "Book a flight to Paris next week and find a hotel under 10 euros per night." This capability requires a deep understanding of natural language and the ability to extract intentions and relevant details from the user's request. Whether the voice assistant can find such a hotel, however, is another question.
NLG is the process where machines independently create human language. While NLP and NLU focus on understanding and interpreting human language, NLG is about producing fluent, understandable texts from data and information. This can range from simple summaries to detailed reports. NLG systems are used in automated reporting, where narrative texts are generated from structured data such as financial reports, weather data, or sports statistics. A weather app that automatically generates weather reports, like "Today will be sunny with a high of 45 degrees," uses NLG to form readable sentences from the raw weather data. However, you'll still have to decide for yourself whether it's a good idea to go outside in that weather.
Sentiment Analysis is an NLP technique that aims to identify and classify the emotional tone behind a text. It's frequently used to analyze opinions, evaluations, and moods in large volumes of online texts, such as product reviews, social media, or news articles. Companies use sentiment analysis to understand public opinion on products, services, or brands, and to gauge customer satisfaction and market trends. An example is analyzing customer feedback on e-commerce platforms to extract positive and negative opinions, improving product development and the customer experience.
Speech recognition refers to the ability of computers to understand spoken words and convert them into textual data. This technology allows users to communicate with machines via natural language and finds wide application, from interactive voice response systems to personal voice assistants like Alexa or Siri. A specific example is the conversion of speech to text for people with hearing impairments, which significantly reduces communication barriers and improves access to information.
TTS technologies convert written text into spoken words. This enables computers to convey information orally to users, which is particularly useful for visually impaired people. However, TTS is also used in situations where reading text is impractical. Modern TTS systems can generate natural and fluid speech that is adapted in real-time to reflect emotions or emphasis. One application example is the read-aloud function in e-readers, which allows users to "listen" to books while performing other tasks. Or because they're too lazy to read. But who would want to admit that?
MT describes the technology that allows computers to automatically translate texts or spoken words from one language to another. This field has made enormous progress thanks to developments in artificial intelligence and machine learning , enabling us today to translate websites, documents, and even live conversations almost in real-time. Machine translation systems, such as Google Translate or DeepL, use complex algorithms to grasp the meaning of the original text and transfer it into the target language as accurately and naturally as possible. Regardless of whether it makes sense, they could instantly translate your tax declaration into Catalan or your shopping list into Vietnamese. As translation systems become increasingly sophisticated, they less frequently overlook nuances and cultural contexts, which means the translations are continually improving.
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