Natural language processing (NLP) is the study of how computers can understand and process human language. The goal of NLP is to make computers as good at understanding natural language as people are and to do this in a way that does not require any special knowledge or training on the part of the computer.
Natural Language Processing has become an important area of research because it promises to enable computers to perform tasks that are difficult for them today, such as translating documents from one language into another, recognizing speech and text in noisy environments, and performing other kinds of pattern recognition tasks.
Natural Language Processing Tasks
Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it's ingesting. Here are five tasks it performs;
The computer breaks a sentence into its component words, which it then analyzes to determine whether they're likely to be nouns or verbs. This task can also be done on voice data; that's how Siri works.
Named entity recognition
NLP programs look at the parts of sentences to see if they match up with pre-defined categories such as people, places, and things such as the Eiffel Tower. If so, the program assigns a name to those entities to make it easier for humans to understand when we speak about them. For example, if you ask Siri where she was born (Where were you born?), she'll say, "I was born in Cupertino." She doesn't know more than that. She knows which category matches what part of your question, but it makes her sound much more intelligent than if she'd said "Cupertino."
When you ask a question like "What time does Apple open tomorrow?" or "How many calories are there in an apple?" computers use NLP techniques like named entity recognition and word sense disambiguation, which determines whether two words have similar meanings to answer questions about real-life objects by looking at their names and other attributes such as size or color. They don't always get it right, and sometimes they get it wrong, but they often come close enough that most people would not notice the difference between human and artificial answers.
This is the most widely used NLP technique, and it's what Siri does when you ask her a question like "What time does Apple open tomorrow?" or "How many calories are there in an apple?" The program looks at the words in your sentence to see if they're phonetically similar enough to other phrases she knows; for example, if you say "apple" and then follow it with "open," she'll know that you mean the store. If so, she can use those phrases as clues to figure out what you want her to do.
When people speak two languages, we often have trouble understanding each other because our brains don't process language. To solve this problem, computers translate speech into text using statistical machine translation (SMT), which tries to predict how words will sound based on their meanings and grammatical structure instead of translating word by word from one language into another. Similarly, we hear sounds differently and make meaning based on context rather than grammar rules alone.
Because SMT does not rely on context or human perception of meaning, it's much more accurate than traditional methods such as transliteration or lexical substitution, which replace all instances of a foreign word with its closest equivalent. Google Translate is probably the best-known example of SMT technology; Siri uses a slightly different system called statistical natural language processing (S-NLP) that works similarly but isn't quite as good yet.
Benefits of Natural Language Processing
The ability to extract meaning from text is a very important skill in today's information age, and natural language processing could be used to help us better understand the world around us. This will allow you to build better search engines and other tools that are based on natural language processing. It will allow you to build better search engines and other tools based on natural language processing.
NLP allows humans and computers to do things like automatically translate written documents into another language or even automatically translate between similar languages so we won't have any trouble understanding them. And it would also let machines know how different words relate one-to-another so they can create algorithms for making sense of large amounts of data quickly without having human beings spend hours doing this by hand, which would save time for everyone involved.
Natural language processing can also be used for other purposes, such as the automated translation of written documents into another language or even automatic translation between different languages, which would be helpful when traveling abroad and automatic translation between languages that are similar. A person can speak in their native language to a foreigner, but they can understand each other because of translation.
NLP has been around for over 30 years, and there is enough research. NLP allows us to take advantage of our own human intelligence to extract meaning from large amounts of data quickly and easily so we do not have to spend time doing it manually or by hand when there are much faster ways to extract meaning from the same amount of data. It's also possible that NLP could be used in conjunction with AI (Artificial Intelligence) systems like Watson, which would be able to perform some tasks more efficiently than humans could do without artificial intelligence.
Natural language processing can help you make sense of unstructured data, such as text and audio files. This will allow you to build better search engines and other tools based on natural language processing.